World Petroleum Council
Guidelines for Application
of the Petroleum
Resources Management System
November 2011
Sponsored by:
Society of Petroleum Engineers (SPE)
American Association of Petroleum Geologists (AAPG)
World Petroleum Council (WPC)
Society of Petroleum Evaluation Engineers (SPEE)
Society of Exploration Geophysicists (SEG)
Guidelines for Application of the Petroleum Resources Management System
2
Contents
Introduction ................................................................................................................................................. 4 1.1 Rationale for New Applications Guidelines ............................................................................................. 4 1.2 History of Petroleum Reserves and Resources Definitions .................................................................... 5 Petroleum Resources Definitions, Classification, and Categorization Guidelines .............................. 7 2.1 Introduction ............................................................................................................................................. 7 2.2 Defining a Project .................................................................................................................................... 8 2.3 Project Classification ............................................................................................................................. 10 2.4 Range of Uncertainty Categorization .................................................................................................... 12 2.5 Methods for Estimating the Range of Uncertainty in Recoverable Quantities ...................................... 15 2.6 Commercial Risk and Reported Quantities ........................................................................................... 16 2.7 Project Maturity Subclasses .................................................................................................................. 18 2.8 Reserves Status .................................................................................................................................... 20 2.9 Economic Status ................................................................................................................................... 21 Seismic Applications ................................................................................................................................ 23 3.1 Introduction ........................................................................................................................................... 23 3.2. Seismic Estimation of Reserves and Resources ................................................................................. 24 3.3 Uncertainty in Seismic Predictions........................................................................................................ 31 3.4 Seismic Inversion .................................................................................................................................. 32 Assessment of Petroleum Resources Using Deterministic Procedures ............................................ 35 4.1 Introduction ........................................................................................................................................... 35
4.2 Technical Assessment Principles and Applications .............................................................................. 37
4.3 Summary of Results .............................................................................................................................. 73 Probabilistic Reserves Estimation .......................................................................................................... 78 5.1 Introduction ........................................................................................................................................... 78 5.2 Deterministic Method ............................................................................................................................ 79 5.3 Scenario Method ................................................................................................................................... 79 5.4 Probabilistic Method .............................................................................................................................. 82 5.5 Practical Applications ........................................................................................................................... 88 Aggregation of Reserves.......................................................................................................................... 92 6.1 Introduction ........................................................................................................................................... 92 6.2 Aggregating Over Reserves Levels (Wells, Reservoirs, Fields, Companies, Countries) ..................... 93 6.3 Adding Proved Reserves ...................................................................................................................... 98 6.4 Aggregating Over Resource Classes.................................................................................................. 102 6.5 Scenario Methods ............................................................................................................................... 103 6.6 Normalization and Standardization of Volumes .................................................................................. 107 6.7 Summary—Some Guidelines .............................................................................................................. 108
Petroleum Resources Definitions, Classification, and Categorization Guidelines
3
Evaluation of Petroleum Reserves and Resources ............................................................................. 109 7.1 Introduction ......................................................................................................................................... 109
7.2 Cash-Flow-Based Commercial Evaluations........................................................................................ 109
7.3 Definitions of Essential Terms ............................................................................................................ 110
7.4 Development and Analysis of Project Cash Flows ............................................................................. 113
7.5 Application Example............................................................................................................................ 119
Unconventional Resources Estimation ................................................................................................ 128 8.2 Extra-Heavy Oil ................................................................................................................................... 130 8.3 Bitumen ............................................................................................................................................... 131 8.4 Tight Gas Formations.......................................................................................................................... 134 8.5 Coalbed Methane ................................................................................................................................ 141 8.6 Shale Gas ........................................................................................................................................... 153 8.7 Oil Shale .............................................................................................................................................. 160 8.8 Gas Hydrates ...................................................................................................................................... 160 Production Measurement and Operational Issues .............................................................................. 162 9.1 Introduction ........................................................................................................................................ 162 9.2 Background ........................................................................................................................................ 162 9.3 Reference Point ................................................................................................................................. 164 9.4 Lease Fuel ......................................................................................................................................... 164 9.5 Associated Nonhydrocarbon Components ........................................................................................ 165 9.6 Natural Gas Reinjection ..................................................................................................................... 165 9.7 Underground Natural Gas Storage .................................................................................................... 166 9.8 Production Balancing ......................................................................................................................... 166 9.9 Shared Processing Facilities .............................................................................................................. 167 9.10 Hydrocarbon Equivalence Issues ..................................................................................................... 168 Resources Entitlement and Recognition .............................................................................................. 172 10.1 Foreword ........................................................................................................................................... 172 10.2 Introduction ....................................................................................................................................... 172 10.3 Regulations, Standards, and Definitions ........................................................................................... 173 10.4 Reserves and Resources Recognition.............................................................................................. 174 10.5 Agreements and Contracts ............................................................................................................... 176 10.6 Example Cases ................................................................................................................................. 182 10.7 Conclusions....................................................................................................................................... 189 Reference Terms ..................................................................................................................................... 191 Guidelines for Application of the Petroleum Resources Management System
4
Chapter 1
Introduction
Satinder Purewal
1.1 Rationale for New Applications Guidelines
SPE has been at the forefront of leadership in developing common standards for petroleum
resource definitions. There has been recognition in the oil and gas and mineral extractive
industries for some time that a set of unified common standard definitions is required that can be
applied consistently by international financial, regulatory, and reporting entities. An agreed set of
definitions would benefit all stakeholders and provide increased
• Consistency
• Transparency
• Reliability
A milestone in standardization was achieved in 1997 when SPE and the World Petroleum
Council (WPC) jointly approved the “Petroleum Reserves Definitions.” Since then, SPE has
been continuously engaged in keeping the definitions updated. The definitions were updated in
2000 and approved by SPE, WPC, and the American Association of Petroleum Geologists
(AAPG) as the “Petroleum Resources Classification System and Definitions.” These were
updated further in 2007 and approved by SPE, WPC, AAPG, and the Society of Petroleum
Evaluation Engineers (SPEE). This culminated in the publication of the current “Petroleum
Resources Management System,” globally known as PRMS. PRMS has been acknowledged as
the oil and gas industry standard for reference and has been used by the US Securities and
Exchange Commission (SEC) as a guide for their updated rules, “Modernization of Oil and Gas
Reporting,” published 31 December 2008.
SPE recognized that new applications guidelines were required for the PRMS that would
supersede the 2001 Guidelines for the Evaluation of Petroleum Reserves and Resources. The
original guidelines document was the starting point for this work, and has been updated
significantly with addition of the following new chapters:
• Estimation of Petroleum Resources Using Deterministic Procedures (Chap. 4)
• Unconventional Resources (Chap. 8)
In addition, other chapters have been updated to reflect current technology and enhanced
with examples. The document has been considerably expanded to provide a useful handbook for
many reserves applications. The intent of these guidelines is not to provide a comprehensive
document that covers all aspects of reserves calculations because that would not be possible in a
short, precise update of the 2001 document. However, these expanded new guidelines serve as a
very useful reference for petroleum professionals.
Chap. 2 provides specific details of PRMS, focusing on the updated information. SEG Oil
and Gas Reserves Committee has taken an active role in the preparation of Chap. 3, which
addresses geoscience issues during evaluation of resource volumes. The chapter has been
Petroleum Resources Definitions, Classification, and Categorization Guidelines
5
specifically updated with recent technological advances. Chap. 4 covers deterministic estimation
methodologies in considerable detail and can be considered as a stand-alone document for
deterministic reserves calculations. Chap. 5 covers approaches used in probabilistic estimation
procedures and has been completely revised. Aggregation of petroleum resources within an
individual project and across several projects is covered in Chap. 6, which has also been updated.
Chap. 7 covers commercial evaluations.
Chap. 8 addresses some special problems associated with unconventional reservoirs, which
have become an industry focus in recent years. The topics covered in this chapter are a work in
progress, and only a high-level overview could be given. However, detailed sections on coalbed
methane and shale gas are included. The intent is to expand this chapter and add details on heavy
oil, bitumen, tight gas, gas hydrates as well as coalbed methane and shale as the best practices
evolve.
Production measurement and operations issues are covered in Chapter 9 while Chapter 10
contains details of resources entitlement and ownership considerations. The intent here is not to
provide a comprehensive list of all scenarios but furnish sufficient details to provide guidance on
how to apply the PRMS.
A list of Reference Terms used in resources evaluations is included at the end of the
guidelines. The list does not replace the PRMS Glossary, but is intended to indicate the chapters
and sections where the terms are used in these Guidelines.
1.2 History of Petroleum Reserves and Resources Definitions
Ron Harrell
The March 2007 adoption of PRMS by SPE and its three cosponsors, WPC, AAPG, and SPEE,
followed almost 3 years and hundreds of hours of volunteer efforts of individuals representing
virtually every segment of the upstream industry and based in at least 10 countries. Other
organizations were represented through their observers to the SPE Oil and Gas Reserves
Committee (OGRC), including the US Energy Information Agency (EIA), the International
Accounting Standards Board (IASB), and the Society of Exploration Geophysicists (SEG). SEG
later endorsed PRMS. The approval followed a 100-day period during which comments were
solicited from the sponsoring organizations, oil companies (IOCs and NOCs), regulators,
accounting firms, law firms, the greater financial community, and other interested parties.
AAPG was founded in 1917; SPE began as part of AIME in 1922, and became an
autonomous society in 1957; WPC began in 1933; and SPEE was created in 1962. Active
cooperation between these organizations, particularly involving individuals holding joint
membership in two or more of these organizations, has been ongoing for years but was not
formally recognized until now.
The initial efforts at establishing oil reserves definitions in the US was led by the American
Petroleum Institute (API). At the beginning of World War I (WWI), the US government formed
the National Petroleum War Service Committee (NPWSC) to ensure adequate oil supplies for the
war effort. At the close of WWI, the NPWSC was reborn as the API. In 1937, API created
definitions for Proved oil reserves that they followed in their annual estimates of US oil reserves.
Little attention was paid to natural gas reserves until after 1946 when the American Gas
Association (AGA) created similar definitions for Proved gas reserves.
SPE’s initial involvement in establishing petroleum reserves definitions began in 1962
following a plea from US banks and other investors for a consistent set of reserves definitions
Guidelines for Application of the Petroleum Resources Management System
6
that could be both understood and relied upon by the industry in financial transactions where
petroleum reserves served as collateral. Individual lenders and oil producers had their own “inhouse” definitions, but these varied widely in content and purpose. In 1962, the SPE Board of
Directors appointed a 12-man committee of well-recognized and respected individuals. They
were known as a “Special Committee on Definitions of Proved Reserves for Property
Evaluation.” The group was composed of two oil producers, one pipeline company, one
university professor, two banks, two insurance companies (lenders), and four petroleum
consultants.
These learned men collaborated over a period of 3 years, debating the exact wording and
terms of their assignment before submitting their single-page work product to the SPE Board in
1965. The SPE Board adopted the committee’s recommendation by a vote of seven in favor,
three dissenting, and two abstaining. The API observer was supportive; the AGA observer
opposed the result.
In 1981, SPE released updated Proved oil and gas definitions that contained only minor
revisions of the initial 1965 version.
The 1987 SPE petroleum reserves definitions were the result of an effort initiated by SPEE,
but ultimately were developed and sponsored by SPE. These definitions, issued for the first time
by a large professional organization, included recognition of the unproved categories of Probable
and Possible Reserves. Much discussion centered around the use of probabilistic assessment
techniques as a supplement or alternative to more-traditional deterministic methods. Following
the receipt of comments from members worldwide, and in particular from North America, the
SPE Board rejected the inclusion of any discussion about probabilistic methods of reserves
evaluation in the 1987 definitions. As a consequence, these definitions failed to garner
widespread international acceptance and adoption.
The 1997 SPE/WPC reserves definitions grew out of a cooperative agreement between WPC
and SPE and appropriately embraced the recognition of probabilistic assessment methods. AAPG
became a sponsor of and an integral contributor to the 2000 SPE/WPC/AAPG reserves and
resources definitions. The loop of cooperation was completed in 2007 with recognition of SPEE
as a fourth sponsoring society.
This recitation is not intended to omit or minimize the creative influence of numerous other
individuals, organizations, or countries who have made valuable contributions over time to the
derivation of petroleum resources definitions out of an initial mining perspective. Further, the
PRMS sponsors recognize the “evergreen” nature of reserves and resources definitions and will
remain diligent in working toward periodic updates and improvements.
Future Updates. Next time PRMS is reviewed and updated, it may be worth considering
inclusion and recognition of 1U, 2U, and 3U as alternative acronyms for Prospective Resources
estimates for low, best, and high in a similar fashion to 1P, 2P, and 3P, and 1C, 2C, and 3C. All
stakeholder societies should encourage the use of the project maturity subclasses to link reservoir
recognition to investment decisions, investment approvals, and field development plans, as
discussed in Chapter 2.
Petroleum Resources Definitions, Classification, and Categorization Guidelines
7
Chapter 2
Petroleum Resources Definitions,
Classification, and Categorization
Guidelines
James G. Ross
2.1 Introduction
PRMS is a fully integrated system that provides the basis for classification and categorization of
all petroleum reserves and resources. Although the system encompasses the entire resource base,
it is focused primarily on estimated recoverable sales quantities. Because no petroleum quantities
can be recovered and sold without the installation of (or access to) the appropriate production,
processing, and transportation facilities, PRMS is based on an explicit distinction between (1) the
development project that has been (or will be) implemented to recover petroleum from one or
more accumulations and, in particular, the chance of commerciality of that project; and (2) the
range of uncertainty in the petroleum quantities that are forecast to be produced and sold in the
future from that development project.
This two-axis PRMS system is illustrated in Fig. 2.1.
1P
Proved
2P
Probable
3P
Possible
CONTINGENT
RESOURCES
1C
2C
3C
PROSPECTIVE
RESOURCES
Low
Estimate
Best
Estimate
Increasing Chance of Commerciality
COMMERCIAL
SUB-COMMERCIAL
DISCOVERED PIIP
RESERVES
UNRECOVERABLE
UNDISCOVERED PIIP
TOTAL PETROLEUM INITIALLY-IN-PLACE (PIIP)
PRODUCTION
High
Estimate
UNRECOVERABLE
Range of Uncertainty
Not to scale
Fig. 2.1—Resources classification framework.
Guidelines for Application of the Petroleum Resources Management System
8
Each project is classified according to its maturity or status (broadly corresponding to its
chance of commerciality) using three main classes, with the option to subdivide further using
subclasses. The three classes are Reserves, Contingent Resources, and Prospective Resources.
Separately, the range of uncertainty in the estimated recoverable sales quantities from that
specific project is categorized based on the principle of capturing at least three estimates of the
potential outcome: low, best, and high estimates.
For projects that satisfy the requirements for commerciality (as set out in Sec. 2.1.2 of
PRMS), Reserves may be assigned to the project, and the three estimates of the recoverable sales
quantities are designated as 1P (Proved), 2P (Proved plus Probable), and 3P (Proved plus
Probable plus Possible) Reserves. The equivalent categories for projects with Contingent
Resources are 1C, 2C, and 3C, while the terms low estimate, best estimate, and high estimate are
used for Prospective Resources. The system also accommodates the ability to categorize and
report Reserve quantities incrementally as Proved, Probable, and Possible, rather than using the
physically realizable scenarios of 1P, 2P, and 3P.
Historically, as discussed in Chap. 1, there was some overlap (and hence ambiguity) between
the two distinct characteristics of project maturity and uncertainty in recovery, whereby Possible
Reserves, for example, could be classified as such due to either the possible future
implementation of a development project (reflecting a project maturity consideration) or as a
reflection of some possible upside in potential recovery from a project that had been committed
or even implemented (reflecting uncertainty in recovery). This ambiguity has been removed in
PRMS and hence it is very important to understand clearly the basis for the fundamental
distinction that is made between project classification and reserve/resource categorization.
2.2 Defining a Project
PRMS is a project-based system, where a project: “Represents the link between the petroleum
accumulation and the decision-making process, including budget allocation. A project may, for
example, constitute the development of a single reservoir or field, or an incremental development
in a producing field, or the integrated development of a group of several fields and associated
facilities with a common ownership. In general, an individual project will represent a specific
maturity level at which a decision is made on whether or not to proceed (i.e., spend money), and
there should be an associated range of estimated recoverable resources for that project.”
A project may be considered as an investment opportunity. Management decisions reflect the
selection or rejection of investment opportunities from a portfolio based on consideration of the
total funds available, the cost of the specific investment, and the expected outcome (in terms of
value) of that investment. The project is characterized by the investment costs (i.e., on what the
money will actually be spent) and provides the fundamental basis for portfolio management and
decision making. In some cases, projects are implemented strictly on the basis of strategic drivers
but are nonetheless defined by these financial metrics. The critical point is the linkage between
the decision to proceed with a project and the estimated future recoverable quantities associated
with that project.
Defining the term “project” unambiguously can be difficult because its nature will vary with
its level of maturity. For example, a mature project may be defined in great detail by a
comprehensive development plan document that must be prepared and submitted to the host
government or relevant regulatory authority for approval to proceed with development. This
document may include full details of all the planned development wells and their locations,
specifications for the surface processing and export facilities, discussion of environmental
Petroleum Resources Definitions, Classification, and Categorization Guidelines
9
considerations, staffing requirements, market assessment, estimated capital, operating and site
rehabilitation costs, etc. In contrast, the drilling of an exploration prospect represents a project
that could become a commercial development if the well is successful. The assessment of the
economic viability of the exploration project will still require a view of the likely development
scheme, but the development plan will probably be specified only in very broad conceptual terms
based on analogues.
In all cases, the decision to proceed with a project requires an assessment of future costs,
based on an evaluation of the necessary development facilities, to determine the expected
financial return from that investment. In this context, the development facilities include all the
necessary production, processing, and transportation facilities to enable delivery of petroleum
from the accumulation(s) to a product sales point (or to an internal transfer point between
upstream operations and midstream/downstream operations). It is these development facilities
that define the project because it is the planned investment of the capital costs that is the basis for
the financial evaluation of the investment and hence the decision to proceed (or not) with the
project. Evaluation of the estimated recoverable sales quantities, and the range of uncertainty in
that estimate, will also be key inputs to the financial evaluation, and these can only be based on a
defined development project.
A project may involve the development of a single petroleum accumulation, or a group of
accumulations, or there may be more than one project implemented on a single accumulation.
The following are some examples of projects:
a. Where a detailed development plan is prepared for partner and/or government approval, the
plan itself defines the project. If the plan includes some optional wells that are not subject to
a further capital commitment decision and/or government approval, these would not
constitute a separate project, but would form part of the assessment of the range of
uncertainty in potentially recoverable quantities from the project.
b. Where a development project is defined to produce oil from an accumulation that also
contains a significant gas cap and the gas cap development is not an integral part of the oil
development, a separate gas development project should also be defined, even if there is
currently no gas market.
c. Where a development plan is based on primary recovery only, and a secondary recovery
process is envisaged but will be subject to a separate capital commitment decision and/or
approval process at the appropriate time, it should be considered as two separate projects.
d. Where decision making is entirely on a well-by-well basis, as may be the case in mature
onshore environments, and there is no overall defined development plan or any capital
commitment beyond the current well, each well constitutes a separate project.
e. Where late-life installation of gas-compression facilities is included in the original approved
development plan, it is part of a single gas development project. Where compression was not
part of the approved plan and is technically feasible, but will require economic justification
and a capital commitment decision and/or approval before installation, the installation of gascompression facilities represents a separate project.
f. In the assessment of an undrilled prospect, a risked economic evaluation will be made to
underpin the decision whether to drill. This evaluation must include consideration of a
conceptual development plan in order to derive cost estimates and theoretically recoverable
quantities (Prospective Resources) on the basis of an assumed successful outcome from the
exploration well (see also discussion of commercial risk in Sec. 2.5). The project is defined
by the exploration well and the conceptual development plan.
Guidelines for Application of the Petroleum Resources Management System
10
g. In some cases, an investment decision may be requested of management that involves a
combination of exploration, appraisal, and/or development activities. Because PRMS
subdivides resource quantities on the basis of three main classes that reflect the distinction
between these activities (i.e., Reserves, Contingent Resources, and Prospective Resources), it
is appropriate in such cases to consider that the investment decision is based on
implementing a group of projects, whereby each project can fit uniquely into one of the three
classes.
Projects may change in character over time and can aggregate or subdivide. For example, an
exploration project may initially be defined on the basis that, if a discovery is made, the
accumulation will be developed as a standalone project. However, if the discovery is smaller
than expected and perhaps is unable to support an export pipeline on its own, the project might
be placed in “inventory” and delayed until another discovery is made nearby, and the two
discoveries could be developed as a single project that is able to justify the cost of the pipeline.
The subsequent investment decision is then based on proceeding with the development of the
two accumulations simultaneously using shared facilities (the pipeline), and the combined
development plan then constitutes the project. Again, the key is that the project is defined by the
basis on which the investment decision is made.
Similarly, a discovered accumulation may initially be considered as a single development
opportunity and then subsequently be subdivided into two or more distinct projects. For example,
the level of uncertainty (e.g., in reservoir performance) may be such that it is considered more
prudent to implement a pilot project first. The initial concept of a single field development
project then becomes two separate projects: the pilot project and the subsequent development of
the remainder of the field, with the latter project contingent on the successful outcome of the
first.
A key strength of using a project-based system like PRMS is that it encourages the
consideration of all possible technically feasible opportunities to maximize recovery, even
though some projects may not be economically viable when initially evaluated. These projects
are still part of the portfolio, and identifying and classifying them ensures that they remain
visible as potential investment opportunities for the future. The quantities that are estimated to be
Unrecoverable should be limited to those that are currently not technically recoverable. A
proportion of these Unrecoverable quantities may of course become recoverable in the future as a
consequence of new technology being developed.
Technology refers to the applied technique by which petroleum is recovered to the surface
and, where necessary, processed into a form in which it can be sold. Some guidelines are
provided in Sec. 2.3 on the relationship between the status of technology under development and
the distinction between Contingent Resources and those quantities that are currently considered
as Unrecoverable.
Finally, it is very important to understand clearly the distinction between the definition of a
project and the assignment of Reserves based on Reserves Status (see Sec. 2.8). Reserves Status
is a subdivision of recoverable quantities within a project and does not reflect a project-based
classification directly unless each well is validly defined as a separate project, as discussed above
in Example d.
2.3 Project Classification
Under PRMS, each project must be classified individually so that the estimated recoverable sales
quantities associated with that project can be correctly assigned to one of the three main classes:
Petroleum Resources Definitions, Classification, and Categorization Guidelines
11
Reserves, Contingent Resources, or Prospective Resources (see Fig. 2.1). The distinction
between the three classes is based on the definitions of (a) discovery and (b) commerciality, as
documented in Secs. 2.1.1 and 2.1.2 of PRMS, respectively. The evaluation of the existence of a
discovery is always at the level of the accumulation, but the assessment of potentially
recoverable quantities from that discovery must be based on a defined (at least conceptually)
project. The assessment of commerciality, on the other hand, can only be performed at a project
level.
Although the definition of “discovery” has been revised to some extent from that contained
in the SPE/WPC/AAPG Guidelines (SPE 2001) for a “known accumulation,” it remains
completely independent from any considerations of commerciality. The requirement is for actual
evidence (testing, sampling, and/or logging) from at least one well penetration in the
accumulation (or group of accumulations) to have demonstrated a “significant quantity of
potentially moveable hydrocarbons.” In this context, “significant” implies that there is evidence
of a sufficient quantity of petroleum to justify estimating the in-place volume demonstrated by
the well(s) and for evaluating the potential for economic recovery.
The use of the phrase “potentially moveable” in the definition of “discovery” is in
recognition of unconventional accumulations, such as those containing natural bitumen, that may
be rendered “moveable” through the implementation of improved recovery methods or by
mining.
Estimated recoverable quantities from a discovery are classified as Contingent Resources
until such time that a defined project can be shown to have satisfied all the criteria necessary to
reclassify some or all of the quantities as Reserves. In cases where the discovery is, for example,
adjacent to existing infrastructure with sufficient excess capacity, and a commercially viable
development project is immediately evident (i.e., by tying the discovery well into the available
infrastructure), the estimated recoverable quantities may be classified as Reserves immediately.
More commonly, the estimated recoverable quantities for a new discovery will be classified as
Contingent Resources while further appraisal and/or evaluation is carried out. In-place quantities
in a discovered accumulation that are not currently technically recoverable may be classified as
Discovered Unrecoverable.
The criteria for commerciality (and hence assigning Reserves to a project) are set out in Sec.
2.1.2 of PRMS and should be considered with care and circumspection. While estimates of
Reserve quantities will frequently change with time, including during the period before
production startup, it should be a rare event for a project that had been assigned to the Reserves
class to subsequently be reclassified as having Contingent Resources. Such a reclassification
should occur only as the consequence of an unforeseeable event that is beyond the control of the
company, such as an unexpected political or legal change that causes development activities to
be delayed beyond a reasonable time frame (as defined in PRMS). Even so, if there are any
identifiable areas of concern regarding receipt of all the necessary approvals/contracts for a new
development, it is recommended that the project remains in the Contingent Resources class until
such time that the specific concern has been addressed.
Contingent Resources may be assigned for projects that are dependent on “technology under
development.” It is recommended that the following guidelines are considered to distinguish
these from quantities that should be classified as Unrecoverable:
1. The technology has been demonstrated to be commercially viable in analogous reservoirs.
Discovered recoverable quantities may be classified as Contingent Resources.
Guidelines for Application of the Petroleum Resources Management System
12
2. The technology has been demonstrated to be commercially viable in other reservoirs that are
not analogous, and a pilot project will be necessary to demonstrate commerciality for this
reservoir. If a pilot project is planned and budgeted, discovered recoverable quantities from
the full project may be classified as Contingent Resources. If no pilot project is currently
planned, all quantities should be classified as Unrecoverable.
3. The technology has not been demonstrated to be commercially viable but is currently under
active development, and there is sufficient direct evidence (e.g., from a test project) to
indicate that it may reasonably be expected to be available for commercial application within
5 years. Discovered Recoverable quantities from the full project may be classified as
Contingent Resources.
4. The technology has not been demonstrated to be commercially viable and is not currently
under active development, and/or there is not yet any direct evidence to indicate that it may
reasonably be expected to be available for commercial application within 5 years. All
quantities should be classified as Unrecoverable.
2.4 Range of Uncertainty Categorization
The “range of uncertainty” (see Fig. 2.1) reflects a range of estimated quantities potentially
recoverable from an accumulation (or group of accumulations) by a specific, defined, project.
Because all potentially recoverable quantities are estimates that are based on assumptions
regarding future reservoir performance (among other things), there will always be some
uncertainty in the estimate of the recoverable quantity resulting from the implementation of a
specific project. In almost all cases, there will be significant uncertainty in both the estimated inplace quantities and in the recovery efficiency, and there may also be project-specific
commercial uncertainties. Where performance-based estimates are used (e.g., based on decline
curve analysis), there must still be some uncertainty; however, for very mature projects, the level
of technical uncertainty may be relatively minor in absolute terms.
In PRMS, the range of uncertainty is characterized by three specific scenarios reflecting low,
best, and high case outcomes from the project. The terminology is different depending on which
class is appropriate for the project, but the underlying principle is the same regardless of the level
of maturity. In summary, if the project satisfies all the criteria for Reserves, the low, best, and
high estimates are designated as Proved (1P), Proved plus Probable (2P), and Proved plus
Probable plus Possible (3P), respectively. The equivalent terms for Contingent Resources are 1C,
2C, and 3C, while the terms “low estimate,” “best estimate,” and “high estimate” are used for
Prospective Resources.
The three estimates may be based on deterministic methods or on probabilistic methods, as
discussed in Chap. 4 and Chap. 5. The relationship between the two approaches is highlighted in
PRMS with the statement that:
“A deterministic estimate is a single discrete scenario within a range of outcomes that
could be derived by probabilistic analysis.”
Further:
“Uncertainty in resource estimates is best communicated by reporting a range of
potential results. However, if it is required to report a single representative result, the
“best estimate” is considered the most realistic assessment of recoverable quantities. It
Petroleum Resources Definitions, Classification, and Categorization Guidelines
13
is generally considered to represent the sum of Proved and Probable estimates (2P)
when using the deterministic scenario or the probabilistic assessment methods.”
The critical point in understanding the application of PRMS is that the designation of
estimated recoverable quantities as Reserves (of any category), or as Contingent Resources or
Prospective Resources, is based solely on an assessment of the maturity/status of an identified
project, as discussed in Sec. 2.3. In contrast, the subdivision of Reserves into 1P, 2P, and 3P (or
the equivalent incremental quantities) is based solely on considerations of uncertainty in the
recovery from that specific project (and similarly for Contingent/Prospective Resources). Under
PRMS, therefore, provided that the project satisfies the requirements to have Reserves, there
should always be a low (1P) estimate, a best (2P) estimate, and a high (3P) estimate, unless some
very specific circumstances pertain where, for example, the 1P (Proved) estimate may be
recorded as zero.
While estimates may be made using deterministic or probabilistic methods (or, for that
matter, using multiscenario methods), the underlying principles must be the same if comparable
results are to be achieved. It is useful, therefore, to keep in mind certain characteristics of the
probabilistic method when applying a deterministic approach:
1. The range of uncertainty relates to the uncertainty in the estimate of Reserves (or Resources)
for a specific project. The full range of uncertainty extends from a minimum estimated
Reserve value for the project through all potential outcomes up to a maximum Reserve value.
Because the absolute minimum and absolute maximum outcomes are the extreme cases, it is
considered more practical to use low and high estimates as a reasonable representation of the
range of uncertainty in the estimate of Reserves. Where probabilistic methods are used, the
P90 and P10 outcomes are typically selected for the low and high estimates. 1
2. In the probabilistic method, probabilities actually correspond to ranges of outcomes, rather
than to a specific scenario. The P90 estimate, for example, corresponds to the situation
whereby there is an estimated 90% probability that the correct answer (i.e., the actual
Reserves) will lie somewhere between the P90 and the P0 (maximum) outcomes. Obviously,
there is a corresponding 10% probability that the correct answer lies between the P90 and the
P100 (minimum) outcome, assuming of course that the evaluation of the full range of
uncertainty is valid. In a deterministic context, “a high degree of confidence that the
quantities will be recovered” does not mean that there is a high probability that the exact
quantity designated as Proved will be the actual Reserves; it means that there is a high degree
of confidence that the actual Reserves will be at least this amount.
3. In this uncertainty-based approach, a deterministic estimate is, as stated in PRMS, a single
discrete scenario that should lie within the range that would be generated by a probabilistic
analysis. The range of uncertainty reflects our inability to estimate the actual recoverable
quantities for a project exactly, and the 1P, 2P, and 3P Reserves estimates are simply single
discrete scenarios that are representative of the extent of the range of uncertainty. In PRMS
there is no attempt to consider a range of uncertainty separately for each of the 1P, 2P, or 3P
scenarios, or for the incremental Proved, Probable, and Possible Reserves, because the
objective is to estimate the range of uncertainty in the actual recovery from the project as a
whole.
1
Under PRMS, the requirement is for the selected cases to be “at least” 90% and 10% probability levels,
respectively.
Guidelines for Application of the Petroleum Resources Management System
14
4. Because the distribution of uncertainty in an estimate of reserves will generally be similar to
a lognormal shape, the correct answer (the actual recoverable quantities) will be more likely
to be close to the best estimate (or 2P scenario) than to the low (1P) or high (3P) estimates.
This point should not be confused with the fact that there is a higher probability that the
correct answer will exceed the 1P estimate (at least 90%) than the probability that it will
exceed the 2P estimate (at least 50%).
For very mature producing projects, it may be considered that there is such a small range of
uncertainty in estimated remaining recoverable quantities that 1P, 2P, and 3P reserves can be
assumed to be equal. Typically, this approach is used where a producing well has sufficient longterm production history that a forecast based on decline curve analysis is considered to be subject
to relatively little uncertainty. In reality, of course, the range of uncertainty is never zero
(especially when considered in the context of remaining quantities), and any assumption that the
uncertainty is not material to the estimate should be carefully considered, and the basis for the
assumption should be fully documented. Note that this is the only circumstance where a project
can have Proved Reserves, but zero Probable and Possible Reserves.
Typically, there will be a significant range of uncertainty and hence there will be low, best,
and high estimates (or a full probabilistic distribution) that characterize the range, whether for
Reserves, Contingent Resources, or Prospective Resources. However, there are specific
circumstances that can lead to having 2P and 3P Reserves, but zero Proved Reserves. These are
described in Sec. 3.1.2 of PRMS.
Conceptually, the framework of PRMS was originally designed on the basis of the
“uncertainty-based philosophy” of reserve estimation [as discussed in Sec. 2.5 of Guidelines for
Evaluation of Reserves and Resources (SPE 2001)], as is clearly demonstrated by its separation
of project maturity from the range of uncertainty and by the simple fact that uncertainty in any
estimate (e.g., reserves attributable to a project) can only be communicated by either a complete
distribution of outcomes derived from probabilistic methodologies or by reporting selected
outcomes (e.g., low, best, and high scenarios) from that distribution, as may be estimated using
deterministic scenario methods. However, as PRMS indicates that the “deterministic
incremental (risk-based) approach” remains a valid methodology in this context, further
explanation is necessary to ensure that this reference is not confused with the “risk-based
philosophy” described in the guidelines (SPE 2001).
As highlighted in the guidelines (SPE 2001), a major limitation of the risk-based philosophy
was that it failed to distinguish between uncertainty in the recoverable quantities for a project
and the risk that the project may not eventually achieve commercial development. Because this
distinction is at the very heart of PRMS, it is clear that such an approach could not be consistent
with the system. In particular, no reserves (of any category) can be assigned unless the project
satisfies all the commerciality criteria for reserves. Thus, for reserves at least, the project should
be subject to very little, if any, commercial risk. The reserve categories are then used to
characterize the range of uncertainty in recoverable quantities from that project.
Provided that the definitions and guidelines specified within PRMS are respected, the
incremental approach (or any other methodology) can be used to estimate reserves or resources.
Estimating discrete quantities associated with each of the three reserves categories (Proved,
Probable, and Possible) remains valid, though it is noted that some of the definitions and
guidelines may still require explicit consideration of deterministic scenarios. For example,
Probable Reserves should be such that: “It is equally likely that actual remaining quantities
Petroleum Resources Definitions, Classification, and Categorization Guidelines
15
recovered will be greater than or less than the sum of the estimated Proved plus Probable
Reserves (2P)” (PRMS Sec. 2.2.2 and Table 3, emphasis added).
2.5 Methods for Estimating the Range of Uncertainty in Recoverable Quantities
There are several different approaches to estimating the range of uncertainty in recoverable
quantities for a project and the terminology is often used in confusing ways. These mathematical
approaches, such as Monte Carlo analysis, largely relate to volumetric methods but are also
relevant to other methodologies. In this context “deterministic” is taken to mean combining a
single set of discrete parameter estimates (gross rock volume, average porosity, etc.) that
represent a physically realizable and realistic combination in order to derive a single, specific
estimate of recoverable quantities. Such a combination of parameters represents a specific
scenario. On this basis, even the probabilistic method is scenario-based. Irrespective of the
approach utilized, the uncertainty in recoverable quantities is associated with the applied (or
planned) project, while the risk (chance of commerciality) of the project is defined by its
assignment to a resource class or subclass.
Keeping in mind that the object of the exercise is to estimate at least three outcomes
(estimated recoverable quantities) that reflect the range of uncertainty for the project, broadly
defined as low, best, and high estimates, it is important to recognize that the underlying
philosophy must be the same, regardless of the approach used. The methods are discussed in
more detail in Chap. 4 and Chap 5.
Evaluators may choose to apply more than one method to a specific project, especially for
more complex developments. For example, three deterministic scenarios may be selected after
reviewing a Monte Carlo analysis of the same project. The following terminology is
recommended for the primary methods in current use:
Deterministic (scenario) method. In this method, three discrete scenarios are developed that
reflect a low, best and high estimate of recoverable quantities. These scenarios must reflect
realistic combinations of parameters and particular care is required to ensure that a reasonable
range is used for the uncertainty in reservoir property averages (e.g., average porosity) and that
interdependencies are accounted for (e.g., a high gross rock volume estimate may have a low
average porosity associated with it). It is generally not appropriate to combine the low estimate
for each input parameter to determine a low case outcome, as this would not represent a realistic
low case scenario (it would be closer to the absolute minimum possible outcome).
Deterministic (incremental) method. The deterministic (incremental) method is widely used
in mature onshore environments, especially where well-spacing regulations apply. Typically,
Proved Developed Reserves are assigned within the drilled spacing-unit and Proved
Undeveloped Reserves are assigned to adjacent spacing-units where there is high confidence in
continuity of productive reservoir. Probable and Possible Reserves are assigned in more remote
areas indicating progressively less confidence. These additional quantities (e.g., Probable
Reserves) are estimated discretely as opposed to defining a Proved plus Probable Reserves
scenario. In such cases, particular care is required to define the project correctly (e.g.,
distinguishing between which wells are planned and which are contingent) and to ensure that all
uncertainties, including recovery efficiency, are appropriately addressed.
Probabilistic method. Commonly, the probabilistic method is implemented using Monte
Carlo analysis. In this case, the user defines the uncertainty distributions of the input parameters
and the relationship (correlations) between them, and the technique derives an output distribution
Guidelines for Application of the Petroleum Resources Management System
16
based on combining those input assumptions. As mentioned above, each iteration of the model is
a single, discrete deterministic scenario. In this case, however, the software determines the
combination of parameters for each iteration, rather than the user, and runs many different
possible combinations (usually several thousand) in order to develop a full probability
distribution of the range of possible outcomes from which three representative outcomes are
selected (e.g., P90, P50 and P10). Stochastic reservoir modeling methods may also be used to
generate multiple realizations.
Multiscenario method. The multiscenario method is a combination of the deterministic
(scenario) method and the probabilistic method. In this case, a significant number of discrete
deterministic scenarios are developed by the user (perhaps 100 or more) and probabilities are
assigned to each possible discrete input assumption. For example, three depth conversion models
may be considered possible, and each one is assigned a probability based on the user’s
assessment of the relative likelihood of each of the models. Each scenario leads to a single
deterministic outcome, and the probabilities for each of the input parameters are combined to
give a probability for that scenario/outcome. Given sufficient scenarios (which may be
supplemented through the use of experimental design techniques), it is possible to develop a full
probability distribution from which the three specific deterministic scenarios that lie closest to
P90, P50 and P10 (for example) may be selected.
2.6 Commercial Risk and Reported Quantities
In PRMS, commercial risk can be expressed quantitatively as the chance of commerciality,
which is defined as the product of two risk components:
1. The chance that the potential accumulation will result in the discovery of petroleum. This is
referred to as the “chance of discovery.”
2. Once discovered, the chance that the accumulation will be commercially developed is
referred to as the “chance of development.”
Because Reserves and Contingent Resources are only attributable to discovered
accumulations, and hence the chance of discovery is 100%, the chance of commerciality
becomes equivalent to the chance of development. Further, and as mentioned previously, for a
project to be assigned Reserves, there should be a very high probability that it will proceed to
commercial development (i.e., very little, if any, commercial risk). Consequently, commercial
risk is generally ignored in the estimation and reporting of Reserves.
However, for projects with Contingent or Prospective Resources, the commercial risk is
likely to be quite significant and should always be carefully considered and documented.
Industry practice in the case of Prospective Resources is fairly well established, but there does
not appear to be any consistency yet for Contingent Resources.
Consider, first, industry practice for Prospective Resources. The chance of discovery is
assessed based on the probability that all the necessary components for an accumulation to form
(hydrocarbon source, trap, migration, etc.) are present. Separately, an evaluation of the potential
size of the discovery is undertaken. Typically, this is performed probabilistically and leads to a
full distribution of the range of uncertainty in potentially recoverable quantities, given that a
discovery is made. Because this range may include some outcomes that are below the economic
threshold for a commercially viable project, the probability of being above that threshold is used
to define the chance of development, and hence a chance of commerciality is obtained by
multiplying this by the chance of discovery. The distribution of potential outcomes is then
Petroleum Resources Definitions, Classification, and Categorization Guidelines
17
recomputed for the “success case;” i.e., for a discovery that is larger than the economic
threshold.
Because Prospective Resources are generally not reported externally, companies have
established their own internal systems for documenting the relationship between risk and
expected outcomes. Usually, if a single number is captured, it would be the “risked mean” or
“risked mean success volume,” where the risk is the chance of commerciality and the mean is
taken from the distribution of recoverable quantities for the “success case.” Note that it is
mathematically invalid to determine a P90 of the risked success-case distribution (or any other
probability level other than the mean itself) by multiplying an unrisked success-case P90 by the
chance of commerciality.
It would be easy to assume that a similar process could be applied for Contingent Resources
to determine a “success case” outcome, based on the probability that the estimated recoverable
quantities are above a minimum economic threshold, but this would not be correct.
Once a discovery has been made, and a range of technically recoverable quantities has been
assessed, these will be assigned as Contingent Resources if there are any contingencies that
currently preclude the project from being classified as commercial. If the contingency is purely
nontechnical (such as a problem getting an environmental approval, for example), the uncertainty
in the estimated recoverable quantities generally will not be impacted by the removal of the
contingency. The Contingent Resource quantities (1C, 2C, and 3C) should theoretically move
directly to 1P, 2P, and 3P Reserves once the contingency is removed, provided of course that all
other criteria for assigning Reserves have been satisfied and the planned recovery project has not
changed in any way. In this example, the chance of commerciality is the probability that the
necessary environmental permit will be obtained.
However, another possible contingency precluding a development decision could be that the
estimated 1C quantities are considered to be too small to commit to the project, even though the
2C level is commercially viable. It is not uncommon, for example, for a company to first test that
the 2C estimate satisfies all their corporate hurdles and then, as a project robustness test, to
require that the low (1C) outcome is at least break-even. If the project fails this latter test and
development remains contingent on satisfying this break-even test, further data acquisition
(probably appraisal drilling) would be required to reduce the range of uncertainty first. In such a
case, the chance of commerciality is the probability that the appraisal efforts will increase the
low (1C) estimate above the break-even level, which is not the same as the probability (assessed
before the additional appraisal) that the actual recovery will exceed the break-even level. In this
situation, because the project will not go ahead unless the 1C estimate is increased, the “success
case” range of uncertainty is different from the pre-appraisal range.
As mentioned above, there is no industry standard for the reporting of Contingent Resource
estimates. However, the commercial risk associated with such projects can vary widely, with
some being "almost there" with, say, an 80% chance of proceeding to development, while others
might have a less than, say, 30% chance. If Contingent Resources are reported externally, the
commercial risk can be communicated to users (e.g., investors) by various means, including: (1)
describing the specific contingencies associated with individual projects; (2) reporting a
quantitative chance of commerciality for each project; and/or (3) assigning each project to one of
the Project Maturity subclasses (see Sec. 2.7).
Aggregation of quantities that are subject to commercial risk raises further complications,
which are discussed in Chap. 6.
Guidelines for Application of the Petroleum Resources Management System
18
2.7 Project Maturity Subclasses
Under PRMS, identified projects must always be assigned to one of the three classes: Reserves,
Contingent Resources, or Prospective Resources. Further subdivision is optional, and three
subclassification systems are provided in PRMS that can be used together or separately to
identify particular characteristics of the project and its associated recoverable quantities. The
subclassification options are project maturity subclasses, reserves status, and economic status.
As illustrated in Fig. 2.2, development projects (and their associated recoverable quantities)
may be subclassified according to project maturity levels and the associated actions (business
decisions) required to move a project toward commercial production. This approach supports
managing portfolios of opportunities at various stages of exploration and development and may
be supplemented by associated quantitative estimates of chance of commerciality, as discussed in
Sec. 2.6. The boundaries between different levels of project maturity may align with internal
(corporate) project “decision gates,” thus providing a direct link between the decision-making
process within a company and characterization of its portfolio through resource classification.
This link can also act to facilitate the consistent assignment of appropriate quantified risk factors
for the chance of commerciality.
PRODUCTION
Project Maturity
Sub-classes
Approved for
Development
Justified for
Development
Development Pending
CONTINGENT
RESOURCES
Development Unclarified
or On Hold
Development
not Viable
Prospect
PROSPECTIVE
RESOURCES
Lead
Increasing Chance of Commerciality
COMMERCIAL
SUB-COMMERCIAL
DISCOVERED PIIP
RESERVES
UNRECOVERABLE
UNDISCOVERED PIIP
TOTAL PETROLEUM INITIALLY-IN-PLACE (PIIP)
On Production
Play
UNRECOVERABLE
Range of Uncertainty
Not to scale
Fig. 2.2—Subclasses based on project maturity.
Evaluators may adopt alternative subclasses and project maturity modifiers to align with their
own decision-making process, but the concept of increasing chance of commerciality should be a
key enabler in applying the overall classification system and supporting portfolio management.
Note that, in quantitative terms, the “chance of commerciality” axis shown in Figs. 2.1 and 2.2 is
not intended to represent a linear scale, nor is it necessarily wholly sequential in the sense that a
Contingent Resource project that is classified as “Development not Viable” could have a lower
Petroleum Resources Definitions, Classification, and Categorization Guidelines
19
chance of commerciality than a low-risk prospect, for example. In general, however, quantitative
estimates of the chance of commerciality will increase as a project moves “up the ladder” from
an exploration concept to a field that is producing.
If the subclasses in Fig. 2.2 are adopted, the following general guidelines should be
considered in addition to those documented in Table 1 of PRMS:
1. On Production is self-evident in that the project must be producing and selling petroleum to
market as at the effective date of the evaluation. Although implementation of the project may
not be 100% complete at that date, and hence some of the reserves may still be Undeveloped
(see Sec. 2.8), the full project must have all necessary approvals and contracts in place, and
capital funds committed. If a part of the development plan is still subject to approval and/or
commitment of funds, this part should be classified as a separate project in the appropriate
subclass.
2. Approved for Development requires that all approvals/contracts are in place, and capital
funds have been committed. Construction and installation of project facilities should be
underway or due to start imminently. Only a completely unforeseeable change in
circumstances that is beyond the control of the developers would be an acceptable reason for
failure of the project to be developed within a reasonable time frame.
3. Projects normally would not be expected to be classified as Justified for Development for
very long. Essentially, it covers the period between (a) the operator and its partners agreeing
that the project is commercially viable and deciding to proceed with development on the
basis of an agreed development plan (i.e., there is a “firm intent”), and (b) the point at which
all approvals and contracts are in place (particularly regulatory approval of the development
plan, where relevant) and a “final investment decision” has been made by the developers to
commit the necessary capital funds. In PRMS, the recommended benchmark is that
development would be expected to be initiated within 5 years of assignment to this subclass
(refer to Sec. 2.1.2 of PRMS for discussion of possible exceptions to this benchmark).
4. Development Pending is limited to those projects that are actively subject to project-specific
technical activities, such as appraisal drilling or detailed evaluation that is designed to
confirm commerciality and/or to determine the optimum development scenario. In addition,
it may include projects that have nontechnical contingencies, provided these contingencies
are currently being actively pursued by the developers and are expected to be resolved
positively within a reasonable time frame. Such projects would be expected to have a high
probability of becoming a commercial development (i.e., a high chance of commerciality).
5. Development Unclarified or On Hold comprises two situations. Projects that are classified
as On Hold would generally be where a project is considered to have at least a reasonable
chance of commerciality, but where there are major nontechnical contingencies (e.g.,
environmental issues) that need to be resolved before the project can move toward
development. The primary difference between Development Pending and On Hold is that in
the former case, the only significant contingencies are ones that can be, and are being,
directly influenced by the developers (e.g., through negotiations), whereas in the latter case,
the primary contingencies are subject to the decisions of others over which the developers
have little or no direct influence and both the outcome and the timing of those decisions is
subject to significant uncertainty.
6. Projects are considered to be Unclarified if they are still under evaluation (e.g., a recent
discovery) or require significant further appraisal to clarify the potential for development,
Guidelines for Application of the Petroleum Resources Management System
20
and where the contingencies have yet to be fully defined. In such cases, the chance of
commerciality may be difficult to assess with any confidence.
7. Where a technically viable project has been assessed as being of insufficient potential to
warrant any further appraisal activities or any direct efforts to remove commercial
contingencies, it should be classified as Development not Viable. Projects in this subclass
would be expected to have a low chance of commerciality.
It is important to note that while the aim is always to move projects “up the ladder” toward
higher levels of maturity, and eventually to production, a change in circumstances (disappointing
well results, change in fiscal regime, etc.) can lead to projects being “downgraded” to a lower
subclass.
One area of possible confusion is the distinction between Development not Viable and
Unrecoverable. A key goal of portfolio management should be to identify all possible
incremental development options for a reservoir; it is strongly recommended that all technically
feasible projects that could be applied to a reservoir are identified, even though some may not be
economically viable at the time. Such an approach highlights the extent to which identified
incremental development projects would achieve a level of recovery efficiency that is at least
comparable to analogous reservoirs. Or, looking at it from the other direction, if analogous
reservoirs are achieving levels of recovery efficiency significantly better than the reservoir under
consideration, it is possible that there are development options that have been overlooked.
A project would be classified as Development not Viable if it is not seen as having sufficient
potential for eventual commercial development, at the time of reporting, to warrant further
appraisal. However, the theoretically recoverable quantities are recorded so that the potential
development opportunity will be recognized in the event of a major change in technology and/or
commercial conditions.
Quantities should only be classified as Unrecoverable if no technically feasible projects have
been identified that could lead to the recovery of any of these quantities. A portion of
Unrecoverable quantities may become recoverable in the future due to the development of new
technology, for example; the remaining portion may never be recovered due to physical/chemical
constraints represented by subsurface interaction of fluids and reservoir rocks. See also the
discussion regarding technology under development in Sec. 2.3.
2.8
Reserves Status
Estimated recoverable quantities associated with projects that fully satisfy the requirements for
Reserves may be subdivided according to their operational and funding status. Under PRMS,
subdivision by reserves status is optional and includes the following status levels: Developed
Producing, Developed Nonproducing, and Undeveloped. In addition, although the prior (1997)
definitions of these subdivisions were associated only with Proved Reserves, PRMS now
explicitly allows the subdivision to be applied to all categories of Reserves (i.e., Proved,
Probable, and Possible).
Reserve status has long been used as a subdivision of Reserves in certain environments, and
it is obligatory under some reporting regulations to subdivide Proved Reserves to Proved
Developed and Proved Undeveloped. In many other areas, subdivision by Reserves status is not
required by relevant reporting regulations and is not widely used by evaluators. Unless mandated
by regulation, it is up to the evaluator to determining the usefulness of these, or any of the other,
subdivisions in any particular situations.
Petroleum Resources Definitions, Classification, and Categorization Guidelines
21
Subdivision by reserves status or by project maturity subclasses is optional and, because they
are to some degree independent of each other, both can be applied together. Such an approach
requires some care, as it is possible to confuse the fact that project maturity subclasses are linked
to the status of the project as a whole, whereas reserves status considers the level of
implementation of the project, essentially on a well-by-well basis. Unless each well constitutes a
separate project, reserves status is a subdivision of Reserves within a project. Reserves status is
not project-based, and hence there is no direct relationship between reserves status and chance of
commerciality, which is a reflection of the level of project maturity.
The relationship between the two optional classification approaches may be best understood
by considering all the possible combinations, as illustrated below. The table shows that a project
that is On Production could have Reserves in all three reserves status subdivisions, whereas all
project Reserves must be Undeveloped if the project is classified as Justified for Development.
Project Maturity
Subclass
Developed
Producing
Reserves
Reserves Status
Developed NonProducing
Reserves
Undeveloped
Reserves
On Production
Approved for
Development
⌧
Justified for
Development
⌧
⌧
Applying reserves status in the absence of project maturity subclasses can lead to the mixing
of two different types of Undeveloped Reserves and will hide the fact that they may be subject to
different levels of project maturity:
1. Those Reserves that are Undeveloped simply because implementation of the approved,
committed and budgeted development project is ongoing and drilling of the production wells,
for example, is still in progress at the date of the evaluation; and,
2. Those Reserves that are Undeveloped because the final investment decision for the project
has yet to be made and/or other approvals or contracts that are expected to be confirmed have
not yet been finalized.
For portfolio analysis and decision-making purposes, it is clearly important to be able to
distinguish between these two types of Undeveloped Reserves. By using project maturity
subclasses, a clear distinction can be made between a project that has been Approved for
Development and one that is Justified for Development, but not yet approved.
2.9
Economic Status
A third option for classification purposes is to subdivide Contingent Resource projects on the
basis of economic status, into Marginal or Submarginal Contingent Resources. In addition,
PRMS indicates that, where evaluations are incomplete such that it is premature to clearly define
ultimate chance of commerciality, it is acceptable to note that project economic status is
Guidelines for Application of the Petroleum Resources Management System
22
“undetermined.” As with the classification options for Reserves that are based on reserves status,
this is an optional subdivision that may be used alone or in combination with project maturity
subclasses.
Broadly speaking, one might expect the following approximate relationships between the two
optional approaches:
Project Maturity
Subclass
Additional SubClassification
Economic
Status
Development Pending
Pending
Marginal Contingent
Resources
Development Unclarified
or On Hold
On Hold
Unclarified
Development Not Viable
Not Viable
Undetermined
Sub-marginal
Contingent Resources
2.10 References
Petroleum Resources Management System, SPE, Richardson, Texas, USA (March 2007).
Guidelines for the Evaluation of Reserves and Resources, SPE, Richardson, Texas, USA (2001).
Seismic Applications
23
Chapter 3
Seismic Applications
Jean-Marc Rodriguez *
3.1 Introduction
Geophysical methods, principally seismic surveys, are one of the many tools used by the
petroleum industry to assess the quantity of oil and gas available for production from a field.
The interpretations and conclusions from seismic data are integrated with the analysis of well
logs, pressure tests, cores, geologic depositional knowledge and other information from
exploration and appraisal wells to determine if a known accumulation is commercial and to
formulate an initial field development plan. As development wells are drilled and put on
production, the interpretation of the seismic data is revised and recalibrated to take advantage of
the new borehole information and production histories. Aspects of the seismic interpretation that
initially were considered ambiguous become more reliable and detailed as uncertainties in the
relationships between seismic attributes and field properties are reduced. The seismic data evolve
into a continuously utilized and updated subsurface tool that impacts both estimation of reserves
and depletion planning.
While 2D seismic lines are useful for mapping structures, the uncertainties associated with all
aspects of a seismic interpretation decreases considerably when the seismic data are acquired and
processed as a 3D data volume. Not only does 3D acquisition provide full spatial coverage, but
the 3D processing procedures (seismic migration in particular) are better able to move reflections
to their proper positions in the subsurface, significantly improving the clarity of the seismic
image. In addition, 3D seismic data can provide greater confidence in the prediction of reservoir
continuity away from well control. 3D seismic offers the geoscientist the option to extract a suite
of more complex seismic attributes to further improve the characterization of the subsurface. 3D
data acquisition and processing improve continuously; a recent example is the development of
Wide Azimuth (WAZ) seismic acquisition and processing that provides improvements in
structural definition and signal to noise ratio in complex geologies.
The following discussion focuses on the application of 3D seismic data in the estimation of
Reserve and Resource volumes as classified and categorized by PRMS. However, in some areas,
2D data may still play a crucial role when Prospective Resources are being estimated. Once a
discovery is made, and as an individual asset or project matures, it has become the norm to
acquire 3D seismic data, which provide critical additional information in support of the
estimation of Contingent Resources and/or Reserves. Finally, once a field has been on
production for some time, repeat seismic surveys may be acquired if conditions are suitable. The
*
With key contributions from the following SEG Oil and Gas Reserves Committee members: Patrick Connolly, Henk
Jaap Kloosterman, James Robertson, Bruce Shang, Raphic van der Weiden and Robert Withers.
Guidelines for Application of the Petroleum Resources Management System
24
information from these time-lapse seismic surveys, also known as 4D seismic, are integrated
with performance data and feed into the Reserves and Resource volumes estimates and updates
to the field development plan.
3.2. Seismic Estimation of Reserves and Resources
The interpretations that a geoscientist derives from 3D seismic data can be grouped conveniently
into those that map the structure and geometry of the hydrocarbon trap (including fault related
aspects), those that characterize rock and fluid properties, and those that are directed at
highlighting changes in the distribution of fluids and/or pressure variations, resulting from
production.
3.2.1 Trap Geometry. Trap geometry is determined by the dips and strikes of reservoirs and
seals, the locations of faults and barriers that facilitate or block fluid flow, the shapes and
distribution of the sedimentary bodies that make up a field’s stratigraphy, and the orientations of
any unconformity surfaces that cut through the reservoir. A 3D seismic volume allows an
interpreter to map the trap as a 3D grid of seismic amplitudes reflected from acoustic/elastic
impedance 3 boundaries associated with the rocks and fluids in and around the trap. The
resolution of 3D seismic typically ranges from 12.5 to 50 m laterally and 8 to 40 m vertically,
depending on the depth and properties of the objective reservoir as well as the nature of the
seismic survey acquisition parameters and the details of the subsequent processing. A
geoscientist uses various interpretive techniques available on a computer workstation to analyze
the seismic volume(s). A geoscientist can synthesize a coherent and quite detailed 3D picture of
a trap’s geometry depending on the seismic quality and resolution. Mapping travel times to
selected acoustic/elastic impedance boundaries (geoscientists often call these boundaries seismic
horizons), displaying seismic amplitude variations along these horizons, isochroning between
horizons, noting changes in amplitude and phase continuity through the volume, and displaying
time and/or horizon slices and volumetric renderings of the seismic data in optimized colors and
perspectives all contribute to the detailed picture of the trap’s geometry. Velocity data from
wells, optionally supplemented with seismic velocity data, is used to convert the horizons picked
in time into depth and thickness.
To fully analyze a trap, a geoscientist typically makes numerous cross sections, maps, and
3D visualizations of both the surfaces (bed boundaries, fault planes, and unconformities) and
thicknesses of the important stratigraphic units comprising the trap. In particular, the geometric
configurations of the reservoirs and their adjacent sealing units are carefully defined. The
displays ultimately are distilled to geometric renderings of the single or multiple pools that form
the field. The final product of the trap analysis is a calculation of the reservoir bulk volume of
these pools (which will later be integrated with reservoir properties such as porosity, net-togross, and hydrocarbon saturation to compute an estimate of the original oil and gas in place).
For fields interpreted to be faulted, it may be necessary to classify resource estimates differently
for individual fault blocks. It is important to make a distinction whether the fault that separates
the undrilled fault block from a drilled fault block can be considered a major, potentially sealing
fault or not. This will depend on the analysis of the extent of the fault, the fault throw as well as
3
Acoustic impedance is the product of density and velocity. Since seismic reflection coefficients/strengths change
with angle elastic impedance is sometimes used for oblique incidence.
Seismic Applications
25
an assessment of fault transmissibility. Seismic amplitudes and flat-spots (see 3.2.2) may be
included in this assessment.
3.2.2 Rock and Fluid Properties. The second general application of 3D seismic analysis is
predicting the rock and pore-fluid properties of the reservoir and sometimes its pressure regime.
The reservoir properties that 3D seismic can potentially predict under suitable conditions are
porosity, lithology, presence of gas/oil saturation as well as pressure. Predictions must be
supported by well control and a representative depositional model. Depending on conditions
predictions may be either qualitative or quantitative. Lithology, including net-to-gross, and
porosity can be loosely estimated from a depositional model of the reservoir based on well data,
3D seismic facies analysis, and field analogs. By knowing whether the depositional system is
fluvial, deltaic, deepwater, or another system, a geoscience team can apply general geologic
understanding and predict reservoir porosity to within appropriate ranges from reservoir
analogues.
In some situations more accurate and higher resolution predictions can be made based on
seismic attributes such as amplitude. The use of such seismic attributes requires that
• A relationship exists at log scale between these attributes and specific reservoir
characteristics
• This relationship still exists at seismic scale (which exhibits lower vertical resolution)
• The seismic quality is satisfactory
• A reliable seismic to well tie exists
The geoscientist should work through each of these: first, by demonstrating a relationship
between a log-scale seismic attribute, such as p-wave or s-wave impedance or elastic impedance
and a reservoir property; second, by demonstrating that a useful relationship still exists at seismic
resolution and for the anticipated geometries of the reservoir; third, the geoscientist should
demonstrate that the data quality of the seismic at the reservoir level is good and that, for
example, overburden effects do not obscure or distort the imaging of the reservoir; and finally, it
should be demonstrated that well synthetics (modeled seismic derived from density and sonic
logs) adequately tie the seismic data.
Qualitative predictions such as the stratigraphic extent of a reservoir may be based on
relatively simple attribute extractions supported by well data and analogues. Quantitative
predictions for example of porosity or net-to-gross will need more sophisticated approaches that
compensate for the tuning 4 effects caused by the band-limited nature of the seismic data. These
could be either 2D map based approaches or 3D seismic inversion based. They may involve
either a direct calibration of the seismic attribute to a reservoir property or a two-stage approach
by first estimating the impedance values. The risks and uncertainties of seismic inversion are
discussed in 3.4.
Attributes may be extracted from conventional stacked volumes or, increasingly, from AVO
attribute volumes such as intercept or gradient or linear combinations of the two. This can
improve correlations between the seismic attribute and the reservoir property. Inversion
algorithms make use either AVO volumes or prestack data. In all cases the quality of the track
4
For thin reservoirs, the seismic reflections from the top and the base of the reservoir overlap and interfere
constructively and destructively with each other to such an extent that the two interfaces have no individual
expression; geophysicists call this effect "tuning." The tuning thickness is the bed thickness at which the two seismic
reflections become indistinguishable in time. It is important to know this thickness before one starts interpreting
seismic data. To this end, geophysicists produce tuning models for the relevant seismic data that can act as a guide
for determining the tuning thickness.
Guidelines for Application of the Petroleum Resources Management System
26
record and confidence ranges, either locally within the 3D volume or regionally, will need to be
considered when determining the reliability of seismic based estimates.
The presence of hydrocarbons typically lowers the seismic velocity and density of
unconsolidated to moderately consolidated sandstones and hence modifies the impedance
contrast with surrounding shales relative to the contrast of water bearing sands with the same
shales. Typically this will increase reflectivity but if brine sands are harder than shales, the
reflectivity can be reduced or change polarity. The down-dip limit of this changed reflectivity
will show up as a change of amplitude that conforms with a structural contour.
If the reservoir thickness is above seismic resolution, a reflection from the hydrocarbon/water
contact may be visible as a reflection event known as a ”flat-spot.” Flat-spots are normally
attributed to a depth (unless there is a lateral pressure gradient in the aquifer) but may not be flat
in time.
The field in the example below shows a seismic expression of an apparent oil-water contract
in a high quality oil sand. The normalized seismic amplitude map in Figure 3.1 shows a good fitto-structure of the amplitude change at the apparent oil-water contact. However, some amplitude
variations are present as well at shallower levels, suggesting variability in the lithology. Key
results are shown in the plot on the right in Figure 3.1. The impact of both reservoir thickness as
well as pore-fill on the seismic response can be observed. The outcome to this analysis underpins
the low, best, and high estimates that feed into the resource classification.
Fig. 3.1—Example of using Seismic Technology to assess fluid contacts. The plot on the right shows the results of a
Monte Carlo seismic modeling exercise in which the full range of key uncertainties (reservoir thickness, porosity, net-togross, rock and fluid properties, etc.) were evaluated.
The visibility of hydrocarbon-related amplitude conformance and flat-spots (Direct
Hydrocarbon Indicators or DHIs) may be enhanced through the use of appropriate AVO
volumes. In all cases, seismic rock property analysis should be provided to support the
identification of an event as a DHI to ensure that the strength and polarity of reflections is
consistent with expectations. DHIs must also be shown to be consistent with the trapping
geometry.
Seismic Applications
27
Figure 3.2—Amplitude maps from a deepwater oil field (hot colors are high negative amplitudes). The oil accumulation is
trapped against a fault to the northeast dipping to an oil-water contact (owc) to the southwest. The maps are from a near
offset (left) and far offset (right) volume. The oil-water contact appears as an amplitude increase on the near offsets and
an amplitude decrease on the far offsets. Both run along a structural contour. The response is consistent with the trap
geometry, the depositional model and the seismic rock properties from the well data.
It is usually not possible to distinguish a fully saturated gas accumulation from a partially
saturated column (residual gas) using full stack or conventional (two-term) AVO analysis, so this
may remain as an unresolved risk. Direct estimation of density contrast using higher order AVO
analysis can in principle distinguish between the two, but this is an emerging technology and
would need to be supported by a historical track record.
It is noted that in many other examples, in which the seismic evidence itself is not as
convincing, other data sources (e.g., pressure data, performance data, geologic deposition model)
will also contribute as part of an integrated analysis to achieve comparable confidence of the
recoverable volumes below the Lowest Known Hydrocarbons (LKH), as observed in the wells.
When a known hydrocarbon accumulation is being appraised, seismic flat-spots and/or
seismic amplitude anomalies can be used to increase confidence in fluid contacts when the
following conditions are met:
• The flat-spot and/or seismic amplitude anomaly is clearly visible in the 3D seismic, and
not related to imaging issues.
• Within a single fault block, well logs, pressure, and well test and/or performance data
demonstrate a strong tie between the calculated hydrocarbon/water contact (not
necessarily drilled) and the seismic flat-spot and/or down-dip edge of the seismic
anomaly.
• The spatial mapping of the flat-spot and/or down-dip edge of the amplitude anomaly
within the reservoir fairway fits a structural contour, which usually will be the down-dip
limit of the accumulation.
Seismic amplitude anomalies may also be used to support reservoir and fluid continuity
across a faulted reservoir provided that the following conditions are met:
Guidelines for Application of the Petroleum Resources Management System
28
•
Within the drilled fault block, well logs, pressure, fluid data, and test data demonstrate a
strong tie between the hydrocarbon-bearing reservoir and the seismic anomaly.
• Fault throw is less than reservoir thickness over (part of) the hydrocarbon bearing section
across the fault and the fault is not considered to be a major, potentially sealing, fault.
• The seismic flat-spot or the seismic anomaly is spatially continuous and at the same depth
across the fault.
If these conditions are met, the presence of hydrocarbon in the adjacent fault block above the
seismic flat-spot or seismic amplitude anomaly may be judged sufficiently robust to qualify the
hydrocarbon volumes as within the same known accumulation and thus qualify as reserves. If
these conditions are only partially met, the interpreter must consider the increased level of
uncertainty inherent in the data and appropriately classify the volumes based on the uncertainty
components. Caution should be exercised in assigning reserves and resource classification
categories. The levels of risk and uncertainty should be commensurate the quality of the data,
velocity uncertainty, repeatability, and quality of supporting data.
3.2.3 Surveillance. The third general application of 3D seismic analysis is monitoring changes in
pore-space composition, pressure, and temperature with fluid movement in the reservoir. This
application is often called time-lapse seismic or more commonly as 4D seismic. Surveillance is
possible if one
• Acquires a baseline seismic data-set
• Allows fluid flow to occur through production and/or injection with associated
pressure/temperature changes
• Acquires additional 3D seismic data-sets sometime after the baseline
• Observes differences between the seismic character of the two data-sets in the
reservoir interval
• Demonstrates through seismic modeling and/or rock and fluid physics based on a
relevant set of well log data that the differences are the result of physical changes
related to the hydrocarbon recovery process
One must be careful not to vary seismic acquisition and processing parameters drastically
between surveys and thereby introduce differences between the seismic data sets that can be
mistaken for reservoir effects. One expects that the seismic character of horizons laterally distant
would be virtually identical between the seismic data-sets because background geology would be
much less affected by production/injection than the hydrocarbon interval. Hence, observing the
difference between the data-sets highlights changes caused by depletion/injection in the reservoir
interval (and possibly in the overburden if compaction occurs). Obviously one can acquire a third
or fourth seismic survey and continue the surveillance by comparing successive data-sets to one
another.
Time-lapse seismology impacts estimation of reserves when an extraction procedure changes
a reservoir’s properties sufficiently so that a robust response occurs in the seismic data. For
example, gas injection to pressurize or flood a reservoir produces an expanding seismic
amplitude anomaly around the injection well owing to the same rock physics that causes
naturally occurring gas zones to appear as bright seismic amplitude anomalies. In this case, the
expansion of the seismic bright spot is directly measurable on successive 3D volumes and clearly
shows the movement of the front of the injected gas. Observing where the gas does not flow (i.e.,
where no seismic amplitude changes) highlights areas of the reservoir that are not being swept by
the gas injection.
Seismic Applications
29
As a second example, bypassed oil reserves can be spotted on time-lapse seismic when a
compartment (fault block or other discrete component of the trap) is unaffected by a drop in
reservoir pressure below bubble point (i.e., there is no indication on the seismic of gas coming
out of solution in that particular compartment at the time in the field’s production life when
overall field pressure is dropping below bubble point). When employed in this manner, timelapse seismic identifies isolated pools that previously were believed to be part of the field’s
connected pool or pools.
As a third example, direct detection of the original versus current depth of the oil/water
contact (OWC) in a producing field is easier on time-lapse seismic data-set than on a single dataset because changes of saturation in the interval swept by the water can noticeably alter the
acoustic/elastic impedance of this part of the reservoir. This impedance change can be detected
by time-lapse seismic comparisons. An example of this is given in Figure 3.3 below:
Fig. 3.3—Example of using time-lapse seismic to assess OWC movement.
These OWC changes as derived from the time-lapse seismic results can then subsequently be
mapped out laterally and be used to update the static and dynamic reservoir models that underpin
the Resources and Reserves volumes estimate.
In general, the seismic tool is useful in time-lapse mode as a check on the validity of the
assumptions in the geologic model that is used in a reservoir simulation of fluid flow. Because
seismic monitoring is more spatially specific than pressure monitoring, estimation and extraction
of reserves can be optimized over time by using the seismic to guide detailed simulations of
depletion and to resolve contradictions between the seismic and the reservoir model. In general,
the incorporation of time-lapse seismic results prompt geologic model updates that usually
improve production history matches.
An example to illustrate this is presented below. In this case, time-lapse seismic results
revealed an area in the west of the F block without 4D sweep (Figure 3.4, left panel), different
from what was expected. New spectrally boosted 3D seismic (Figure 3.4, center panel) shows
evidence for a normal fault cutting the F block into two separate blocks. The 3D horizon (Figure
3.4, right panel) shows that the downthrown block corresponds to the same area seen to be
unswept on the time-lapse seismic (left panel).
Guidelines for Application of the Petroleum Resources Management System
30
Fig. 3.4—Time-lapse seismic results indicate the presence of a sealing fault.
The new fault was incorporated in the model update, allowing for an improved history match
by adjusting the fault seal properties. Simulated production data from the northern EF blocks
prior to the time-lapse seismic results (Figure 3.5.lower left panel—solid lines) show a much
later water breakthrough, as compared to actual production data (Figure 3.5 lower left panel—
diamonds). Incorporating the new fault into the model, resulted in the bypassing the block
(Figure 3.5 right panel) and greatly improved the timing of water breakthrough (Figure 3.5 lower
left panel—dotted lines). As a result from incorporating the time-lapse seismic results, the
bypassed volumes in the SW part of block F will have to be reclassified from Developed
Reserves into Contingent Resources until further development activities are in place.
Seismic Applications
31
Fig. 3.5—Integration of time-lapse seismic results into Reservoir Simulation..
3.3 Uncertainty in Seismic Predictions
Predictions from 3D seismic data aimed at defining trap geometry, rock/fluid properties or fluid
flow have an inherent uncertainty. The accuracy of a given seismic-based prediction is
fundamentally dependent on the resulting interplay between
• The quality of the seismic data (bandwidth, frequency content, signal-to-noise ratio,
acquisition and processing parameters, overburden effects, etc.)
• The uncertainty in the rock and fluid properties and the quality of the reservoir model
used to tie subsurface control to the 3D seismic volume
A derived reservoir model that is accurately predicting a subsurface parameter or process as
proven by drilling results from new wells has demonstrated a reduction in uncertainty and the
current level of uncertainty can be revised accordingly after several successful predictions. Such
a reservoir model is far more valuable than an untested reservoir model, even though the latter
may be more sophisticated. Care should be taken extrapolating the results from new wells, if
such programs targeted high amplitude or “sweet spot” and remaining targets are not in a similar
setting. Appropriate consideration should be made regarding predictability.
It is useful to assess the track record of a given 3D seismic volume or of regional analogues
in predicting subsurface parameters at new well locations before drilling. The predictive record
is the best indicator of the degree of confidence with which one can employ the seismic to
estimate reserves and resources as exploration and development proceeds in an area.
The following is a general quantification of the uncertainty in using 3D seismic to estimate
reserves and resources. Specific cases should be analyzed individually with the geophysical and
Guidelines for Application of the Petroleum Resources Management System
32
geology team members to determine if a project’s seismic accuracy is better or worse than this
general quantification.
3.3.1 Gross Rock Volume (GRV) of a Trap. The gross rock volume of a field is defined by
structural elements, such as depth maps and fault planes resulting from an interpretation based on
seismic and well data. Uncertainties in the GRV, and hence in the in-place volumes, reserves
and production profiles, can arise from
• The incorrect positioning of structural elements during the processing of the seismic
• Incorrect interpretation
• Errors in the time to depth conversion
An assessment of these uncertainties is an essential step in a field study for evaluation,
development, or optimization purposes.
It is important to appreciate that the relative uncertainty in predicting depth to a trapping
surface at a new location, once the trap depth is precisely known at initial well locations, is much
less than the errors in predicting trap depth in an exploration setting prior to the drilling of the
first well. That uncertainty generally is tens to hundreds of meters because there is no borehole
control on the vertical velocity from the earth’s surface down to the trap. In addition to the
uncertainties in the velocities, alternative interpretations of the seismic data are the major source
of uncertainties in (green-field) exploration settings, affecting the evaluation of Prospective
Resources.
3.3.2 Reservoir Bulk Volume. If the trap volume under the seal is completely filled with
reservoir rock, the GRV of the trap is of course identical to reservoir bulk volume. Generally,
this is not the case, and the thickness and geometry of the one or more reservoir units within the
trap have to be estimated to derive reservoir bulk volume. The accuracy of the estimate of the
thickness of each reservoir is a critical element in assessment of reserves.
Estimation of reservoir thickness is dependent on the bandwidth and frequency content of the
seismic data and on the seismic velocity of the reservoir. Broadband, high-frequency seismic
data in a shallow clastic section where velocity is relatively slow can resolve a much thinner bed
than, for example, narrow- band, low-frequency seismic data deep in the earth in a fast,
carbonate section. Fortunately, geoscientists can analyze seismic and sonic log data to estimate
what thicknesses can reasonably be measured for particular reservoirs under investigation.
Stacked reservoirs in a trap can be individually resolved and separate reservoir bulk volumes
can be computed if the reservoirs and their intervening seals can be interpreted separately and
individually meet the minimum thickness derived from the relevant tuning model. Under these
conditions, a deterministic estimate of reserves in each reservoir is possible. When the individual
reservoirs and seals are too thin to satisfy these conditions, seismic modeling can be used to get a
general idea of how much hydrocarbons might be present in a gross trapped volume. In some
circumstances it may be possible to detune the seismic response of thin reservoirs to estimate the
total net or gross reservoir. The reliability of these calculations will depend on a number of
factors; bed thicknesses, spacing among beds, porosity variation, etc.
3.4 Seismic Inversion
Standard 3D seismic volumes display seismic amplitude in either travel time or depth.
Conversion of seismic amplitude data to acoustic impedance (product of P-velocity and density)
and shear impedance (product of S-velocity and density) volumes or related elastic parameters is
still a growing field. The conversion process is called seismic inversion. There will typically be
Seismic Applications
33
a relationship between acoustic and shear impedance and lithology, porosity, pore fill and other
factors and hence estimates of these parameters may be derived from an analysis of these
relationships (a rock property model) combined with inverted seismic.
Inverted seismic data focuses on layers rather than interfaces, and some features in the data
may be more obvious or easier to interpret in the inverted format than the conventional format,
so there can be value to analyzing the basic seismic information in both formats.
Inversion requires the seismic to be combined with additional data and hence good-quality
impedance inverted volumes will contain more information than a conventional seismic volume.
Specifically additional data is required to compensate for the lack of low frequencies in the
seismic. However, there will rarely be enough data to fully constrain the low-frequency
component so inversion results will be nonunique. Because of this uncertainty, a probabilistic
approach can be followed to try to capture the full range of possible outcomes. The uncertainty
analysis should cover the nonuniqueness of the inversion process and the uncertainties arising
from the rock property model. The probabilities of the various outcomes can then subsequently
be used as input to Reserves and Resource volume assessments. However, estimating all the
uncertainties in the process is difficult. Use of this technology would need to be supported by a
strong track record. Additionally, a relationship between acoustic impedance or elastic
impedance and petrophysical properties must be established at log scale resolution. The type of
inversion method should also be considered as well as the confidence in the well-based
background model used for generating the low frequency component.
An example of probabilistic seismic inversion is given below. In this example, the key
uncertainty for estimation of in-place volumes is the net sand thickness distribution. Porosity
variation within a reservoir unit is small, although there is a general trend where deeper reservoir
levels have slightly lower porosity. Likewise, variation in oil saturation is small. However,
variation in reservoir thickness and sand percentage is large. Probabilistic inversion was used to
provide a better estimate of net sand distribution, and also to quantify the range of uncertainty.
The inversion works on a layer-based model, where all input data are represented as grids. The
inversion combines in a consistent manner the petrophysical and geologic information with the
seismic data. Probability density functions for reservoir parameters such as layer thickness, netto-gross, porosity and fluid saturations are obtained from well and geologic data with soft
constraints obtained from seismic amplitudes. Using this prior information, the program then
generates numerous subsurface models that match the actual seismic data within the limits set by
the noise that is derived from the seismic data. The net sand maps in Figure 3.6 illustrate the
probabilistic output from the inversion for low, mid, and high cases. Each map fits the well data
used to constrain the model. The three net sand maps reflect the uncertainty in the net sand
distribution and can be used to constrain three different “oil-in-place” scenarios in low-, midand high-case static models that can be carried through to reservoir simulation and are thus key
input to the resource volume assessment and classification.
Guidelines for Application of the Petroleum Resources Management System
34
Fig. 3.6—Model-based, probabilistic seismic inversion provides low, mid, and high scenarios for net sand distribution,
which is the main driver for variation in oil in place estimates.
Additional Reading
Abriel, W.L. 2008. Reservoir Geophysics: Applications, SPE Distinguished Instructor short
course presented at the SEG/EAGE Conference and Exhibition, Rome, 9–12 June.
Brown, A.R. 1999. Interpretation of Three-Dimensional Seismic Data: AAPG Memoir 42, fifth
edition, Tulsa, aka Investigations in Geophysics No. 9, SEG (Joint Publication of AAPG and
SEG).
Calvert, R. 2005. Insights and methods for 4D reservoir monitoring and characterization,
EAGE/SEG Distinguished Instructor Short Course No. 8.
Chapin, M., et al. 2002. Integrated seismic and subsurface characterization of Bonga Field,
offshore Nigeria, The Leading Edge.
Chopra, S. and Marfurt, K.J. 2006. Seismic Attribute Mapping of Structure and Stratigraphy,
SPE Distinguished Instructor short course presented at the SEG/EAGE Conference and
Exhibition, Vienna.
Connolly, P. 2010. Robust Workflows for Seismic Reservoir Characterisation, SEG
Distinguished Lecture.
Hilterman, F.J. 2001. Seismic Amplitude Interpretation, EAGE/SEG Distinguished Instructor
Short Course No. 4.
Jack, I. 1998. Time-Lapse Seismic in Reservoir Management, Distinguished Instructor Series
No. 1, Society of Exploration Geophysicists.
Kloosterman et al. 2010. Chapter 5.3, Methods and Applications in Reservoir Geophysics, SEG
Investigations in Geophysics Series No. 15.
Sheriff, R.E. ed., Reservoir Geophysics, Investigations in Geophysics 7, Society of Exploration
Geophysicists.
Sidle, R. et al. 2010. Qualifying Seismic as a “Reliable Technology”—An Example of Downdip
Water Contact Location, SPE 134237.
Staples, R. et al. 2005. 4D seismic history matching—the reality, EAGE 67th Conference &
Exhibition, Madrid.
Assessment of Petroleum Resources Using Deterministic Procedures
35
Chapter 4
Assessment of Petroleum Resources
Using Deterministic Procedures
Yasin Senturk
4.1 Introduction
This chapter provides additional guidance to the Petroleum Resources Management System
(PRMS) Sec. 4.1 (SPE 2007) regarding the application of three broad categories of deterministic
analytical procedures for estimating the range of recoverable quantities of oil and gas using (a)
analogous methods, (b) volumetric methods, and (c) production performance analysis methods.
During exploration, appraisal, and initial development periods, resource estimates can be
“indirectly” derived only by estimating original in-place volumes using static-data-based
volumetric methods and the associated recovery efficiency based on analog development
projects, or using analytical methods. In the later stages of production, recoverable volumes can
also be estimated “directly” using dynamic-data-based production performance analysis.
It must be recognized that PRMS embraces two equally-valid deterministic approaches to
reserves estimation: the “incremental” approach and the “scenario” approach. Both approaches
are reliable and arrive at comparable results, especially when aggregated at the field level; they
are simply different ways of thinking about the same problem.
In the incremental approach, experience and professional judgment are used to estimate
reserve quantities for each reserves category (Proved, Probable, and Possible) as discrete
volumes. When performing volumetric analyses using the incremental approach, a single value is
adopted for each parameter based on a well-defined description of the reservoir to determine the
in-place, resources, or reserves volumes.
In the scenario approach, three separate analyses are prepared to bracket the uncertainty
through sensitivity analysis (i.e., estimated values by three plausible sets of key input parameters
of geoscience and engineering data). These scenarios are designed to represent the low, the best
(qualitatively considered the most likely) and the high realizations of original in-place and
associated recoverable petroleum quantities. Depending on the stage of maturity, these scenarios
underpin the PRMS categorization of Reserves (1P, 2P, and 3P) and Contingent Resources (1C,
2C, and 3C) of the projects applied to discovered petroleum accumulations, or Prospective
Resources (low, best, and high) of the undiscovered accumulations with petroleum potential.
The advantages of a deterministic approach are (a) it describes a specific case where
physically inconsistent combinations of parameter values can be spotted and removed, (b) it is
direct, easy to explain, and manpower efficient, and (c) there is a long history of use with
estimates that are reliable and reproducible. Because of the last two advantages, investors and
shareholders like the deterministic approach and it is widely used to report Proved Reserves for
regulatory purposes. The major disadvantage of the deterministic approach is that it does not
quantify the likelihood of the low, best and high estimates. Sensitivity analysis is required to
assess both the upside (the high) and the downside (the low) estimates by respectively using
Guidelines for Application of the Petroleum Resources Management System
36
different values of key input reservoir parameters (geoscience and engineering data) to plausibly
reflect that particular realization or scenario.
The guidance in this chapter is focused only on the deterministic methods where the range of
uncertainty is captured primarily using a scenario approach. Chapter 5 provides guidance on
applying probabilistic methods. The goal of this chapter is to promote consistency in reserves
and resources estimates and their classification and categorization using PRMS guidelines.
Fig. 4.1 shows how changes in technical uncertainty impact the selection of applicable
resources assessment method(s) for any petroleum recovery project over its economic life cycle.
Exploration and Appraisal Phase I
Production Phase II
Daily Oil Rate
Best Estimate
Abandonment
Estimated
Ultimate
Recovery (EUR)
Range of Uncertainty
High Estimate
Low Estimate
Cumulative
Production
Ultimate
Recovery
(UR)
Plateau Period
Early
Time
IIa
I
Late
Time
IIc (Decline Period)
IIb
Economic Limit
Time
Volumetric
Methods
Analogy and Analytical Methods
Reservoir Simulation and Material Balance Methods
Production Performance Trend (PPT) Analysis
Fig. 4.1—Change in uncertainty and assessment methods over the project’s E&P life cycle.
Fig 4.1 illustrates that the range of estimated ultimate recovery (EUR) of any petroleum
project decreases over time as the accumulation is discovered, appraised (or delineated),
developed, and produced, with the degree of uncertainty decreasing at each stage. Once
discovered, the duration of each period depends both on the size of accumulation (e.g., appraisal
period) and the development design capacity in terms of annual reservoir depletion rate (e.g., as
% of reserves produced per year). For example, projects with lower depletion rates will support a
relatively longer plateau period followed by a longer decline period, and vice versa. While the
“best estimate” is conceptually illustrated as remaining constant, in actual projects there may be
significant volatility in this estimate over the field appraisal and development life cycle.
Assessment of petroleum recoverable quantities (reserves and resources) can be performed
deterministically by using both indirect and direct analytical procedures, involving the use of the
volumetric-data-based “static” and the performance-data-based “dynamic” methods,
respectively.
Assessment of Petroleum Resources Using Deterministic Procedures
37
The selection of the appropriate method to estimate reserves and resources, and the accuracy
of estimates, depend largely on the following factors:
• The type, quantity, and quality of geoscience, engineering, and economics data available and
required for both technical and commercial analyses.
• Reservoir-specific geologic complexity, the recovery mechanism, stage of development, and
the maturity or degree of depletion.
More importantly, reserves and resources assessment relies on the integrity, skill and judgment
of the experienced professional evaluators.
4.2 Technical Assessment Principles and Applications
This section provides a technical summary description of the appropriate deterministic resource
assessment methods applied to an example oil project in various stages of its maturity, retraced
over its full E&P life cycle as depicted by phases and stages identified in Fig. 4.1. In addition, an
example of reserves assessment of a nonassociated mature gas reservoir is included to
demonstrate the use of the widely practiced production performance-based material balance
method of (p/z) vs. cumulative gas production relationship. The focus is on assessment of risk
and uncertainty and how these are represented by PRMS classes and categories of petroleum
reserves and resources.
4.2.1 Definition of the Example Oil Project—Setting the Stage. Since it is used to
demonstrate the applications of each major assessment method using deterministic procedures, it
is important to set the stage and describe the example oil reservoir and point out its
distinguishing characteristics.
Fig. 4.1a shows the time line and the assessment methods used to estimate the example
project’s in-place and recoverable oil and gas volumes at different stages of project maturity.
Volumetric Analysis
(Exploration, Appraisal
and Development Stages)
Reservoir
Simulation
(Early Decline)
Material Balance
(Early Production)
MBOD
Appraisal and Initial
Development Stages
TODAY (Year 2011)
Decline Curves
(Late Decline)
Assessment Method
(Development Stage)
Production Phase
Decline Period
75
8 Years
50
16 Years
25
26 Years
0
-5
(Discovery)
0
5
10
15
20
25
Time, Years
30
35
40
45
50
Fig. 4.1a—Timeline for example oil project maturity stages and assessment methods used.
The example oil reservoir represents a typical accumulation in a mature petroleum basin
containing extremely large structures with well-established regional reservoir continuity and
numerous adjacent analog development projects. Therefore, the project scale and internal
confidence in reservoir limits may not be typical for assessments carried out in other petroleum
basins. It is a very prolific carbonate reservoir located onshore. Analog projects with varying
sizes have already produced over 60% of their respective EURs from the same geological
formations in the same petroleum basin, all depleted under well-established and effective
peripheral water injection schemes implemented initially at project start-ups.
Guidelines for Application of the Petroleum Resources Management System
38
In general, because of the leverage of having high-quality large oil reservoirs with excess
development potential relative to market needs prevalent in the Middle East, the ways these
reservoirs are developed and produced may be significantly different than those commonly
practiced elsewhere. These reservoirs were developed at relatively low depletion rates, ranging
from 2 to 4% of EUR per year, which means
• Low development size (e.g., level of daily plateau oil production rate) naturally necessitated
reservoir development in stages. For example, instead of drilling most of the well-spacing
units (WSU’s) initially at once to achieve higher daily production rates, it was common to
drill only a fraction (20 to 30%) of them to achieve the target rate. The number of producers
depends on their established Productivity Indices (PIs). As a result, annual drilling continues
over extended periods (sometimes exceeding 50 years) to sustain the target plateau
production rate as long as possible to better manage decline and improve overall reservoir
volumetric sweep efficiency.
• Longer plateau periods are followed by relatively low annual decline rates and longer decline
periods and project economic lives, sometimes exceeding 100 years. In reality, the project
lives will eventually be shortened to 50–70 years as the approaching planned artificial lift and
EOR projects are implemented to both accelerate production (e.g., higher depletion rates) and
increase ultimate recovery. Moreover, longer project lives are very beneficial because:
o It allows the operator to take advantage of new technological applications that may
not be available in other reservoirs with shorter lives and thus potentially benefiting
from lower capital and operating costs. It also defers capital costs for delayed EOR
projects.
o Growth in water production (or water-cut) is relatively low because of peripheral
water injection and low depletion rates. Lower and slow growth in water-cuts help
delay the need for installation of artificial lift facilities and again defers costs.
Note that for purposes of this oil example project, all associated raw gas volumes are deemed
to be transferred to the host government at the wellhead before shrinkage for condensate
recovery and/or subsequent processing to remove nonhydrocarbons and natural gas liquids
(NGLs) to yield marketable natural gas. Thus, gas volumes are excluded from entitlement to the
license holder. For more details, readers should refer to Chapters 9 and 10 on production
measurements, reporting, and entitlement.
Many other important and more complex project-specific issues that may require different
interpretations, judgments, and resolutions by the analysts are not addressed. The main objective
of this chapter is to illustrate the applications of the major petroleum resources assessment
procedures for estimating plausible ranges of project in-place and recoverable quantities that are
deemed to be “reasonable,” “technically valid,” and are “compliant” with PRMS guidance.
4.2.2 Volumetric and Analogous Methods. Static data-based volumetric methods to estimate
petroleum initially in-place (PIIP) and analogous methods to estimate recovery efficiencies are
the indirect estimating procedures used during exploration, discovery, post-discovery, appraisal,
and initial development (or exploitation) stages of the E&P life cycle of any recovery project.
a.) Technical Principles. These procedures may be called “indirect” because the EUR cannot
be derived directly, but requires independent estimates of reservoir-specific PIIP volume and
appropriate recovery efficiency (RE). It is generally expressed in terms of a simple classical
volumetric relationship defined by
EUR (STB or scf) = PIIP (STB or scf) × RE (fraction of PIIP)
(4.1a)
Assessment of Petroleum Resources Using Deterministic Procedures
39
In terms of average variables of area (A), net pay (h), porosity ( φ ), initial water saturation (Swi)
and hydrocarbon formation volume factor (FVF) (Bhi) for oil (RB/STB) or gas (Rcf/scf), the
generalized classic volumetric equation for the PIIP [oil initially in place (OIIP) or gas initially
in place (GIIP)] is given by
PIIP (STB or scf) = A h φ (1- Swi) / Bhi,
(4.1b)
where oil or gas volumes are in barrels or cubic feet, abbreviated as STB and RB or scf and Rcf,
representing the measurements at standard surface (s) and reservoir (R) conditions, respectively,
based on respective pressures and temperatures.
For each petroleum resource category, the estimates of PIIP are determined volumetrically
using Eq. 4.1b. However, an independently estimated RE is necessary to calculate project EUR.
Recovery efficiency may be assigned from appropriate analogs, using analytical methods or, as a
last resort, using published empirical correlations.
PRMS encourages the use of available analogs to assign RE. The rationale for the selection
of analogous reservoirs are well provided for in Cronquist (2001) and Harrell et al. (2004) and in
the PS-CIM publications (2004, 2005, and 2007). Technical principles of natural and
supplementary oil recovery mechanisms and analytical procedures to estimate recovery
efficiency may be found in many references, including Cronquist (2001), Walsh and Lake
(2003), and Dake (1978 and 2001) (for natural reservoir drives); Craig (1971), Smith (1966), and
Sandrea and Nielson (1974) (immiscible water and gas injection schemes for pressure
maintenance); Taber and Martin (1983) [enhanced oil recovery (EOR) screening]; Prats (1982)
and Boberg (1988) (thermal processes); Lake (1989) and Latil (1980) (polymer flooding); and
Dake (1978), Stalkup (1983), Klins (1984), Lake (1989), Green and Willhite (1998), and
Donaldson et al. (1985) (miscible processes and chemical methods of micellar-polymer and
alkaline-polymer flooding). For a quick review, PS-CIM (2004) and Carcoana (1992) are
recommended. Finally, the published empirical correlations to estimate RE can be found in many
references, including Cronquist (2001), Walsh and Lake (2003), and Craig (1971). However, it
should be emphasized that even a rough estimate of recovery efficiency from a near-analog or
determined by using a physically based analytical method is preferable to using empirical
correlations.
With the availability of computational power and integrated work-processes, these analytical
procedures may be supplemented by recovery process-specific reservoir simulation model
studies. Rigorous models may effectively predict not only any reservoir-specific recovery
performance including EOR, but also incorporate the ever-changing recovery enhancing
practices resulting from the successful application of field-tested drilling and completion (e.g.,
multilateral, extended-reach and smart wells with inflow-control devices, etc.), reservoir
development and production engineering technologies that optimize the overall flow system
starting from reservoir through well completions, wellbore and the surface facilities and
pipelines.
b.) Applications to Example Oil Project During Its Exploration and Appraisal Phase and
Initial Development Stage. Geological maps for an example petroleum project during these
phases and different stages within each phase (see Figs. 4.2 through 4.5) were re-created through
a look-back process. These maps were developed and associated net reservoir rock volumes
Guidelines for Application of the Petroleum Resources Management System
40
were estimated by Wang (2010). However, the appraisal and development plans described
estimates of PIIPs and recoverable volumes including the assignment of different categories of
reserves and resources were made by the author.
Excellent guidance on how to construct better maps and minimize mapping errors is provided
by Tearpock and Bischke (1991). Moreover, Harrell et al. (2004) provides an excellent review on
the complex nature of the reserves assessment process, the use of analogs, and recurring mistakes
and errors, including subsurface mapping.
Based on the PRMS definitions and guidelines, assessment and assignment of different
categories of resources and reserves for the example oil project during its E&P life cycle stages
are presented below.
Prediscovery Stage. In the prediscovery stage, the range of Prospective Resources is
estimated based on a combination of volumetric analyses and use of appropriate analogs. The
geological realization of this “exploratory prospect” shown in Fig. 4.2a was developed based on
a combination of seismic and geological studies that define the shape and closure for
potentialpetroleum accumulation. The 2D seismic defined a structural spill point, but provided
no indication of fluid contacts. Based on the analog carbonate reservoirs, it was assumed that this
exploratory petroleum prospect would most likely contain light crude with gravity 30 to 33o API.
The volumetric assessment process starts with the estimate of gross reservoir rock volume
depicted by the cross section presented as Fig. 4.2a. Based on regional analogs, the high estimate
assumed the structure to be fully charged to its spill point at 6,410 ft subsea. The volume above
6,120 ft subsea was assigned conservatively to represent the low estimate and the vertical limit
for the best estimate was set at an intermediate depth of 6,265 ft subsea. Typically, information
on regional and local geology are used to construct net-to-gross (NTG) maps (obtained from the
nearby analog reservoirs after applying parameter cutoffs to exclude portions of the reservoir that
do not meet the minimum criteria to support production), and integrated with gross reservoir
volume to yield net pay maps. In this case, analysts applied a constant average NTG ratio of
0.70. The net pay isochore maps depicted as Figs. 4.2b, 4.2c, and 4.2d were developed,
representing the reservoir pay volumes for low, best, and high estimate scenarios, respectively.
The vertical and areal extent associated with each scenario is illustrated in these maps.
Furthermore, the chance of discovery was estimated at 40% based on independent
assessments of source rock, trap integrity, reservoir adequacy, and regional migration paths. The
chance of such a technical success being commercially developed, or the chance of development,
is estimated at 60% based on analysis of economic scenarios and assessment of other commercial
contingencies. Hence, the overall chance of commerciality of this exploratory prospect, defined
as the product of these two risk components is estimated to be 24%.
Assessment of Petroleum Resources Using Deterministic Procedures
Subsea
Depth (ft)
-6000
A
Planned
Wildcat
A'
-6100
Low Estimate
-6120'
Best Estimate
-6265'
-6200
-6300
-6400
High Estimate
-6410'
Spill Point
-6500
(a) Structural Cross Section AA’ (not to scale horizontally)
(b) Net Pay Isochore Map for Low Estimate
Proposed Wildcat
60’
60
(c) Net Pay Isochore Map for Best Estimate
Contour Lines (ft)
Proposed Wildcat
84’
(d) Net Pay Isochore Map for High Estimate
Proposed Wildcat
84’
0
1
2
Kilometers
Fig. 4.2—Volumetric assessment of Prospective Resources:
prediscovery stage [Wang (2010)].
41
Guidelines for Application of the Petroleum Resources Management System
42
Assuming a discovery, Table 4.1 documents the estimates of average reservoir parameters
(i.e., rock and fluid properties, and a range of recovery efficiencies expected from peripheral
water injection projects already implemented and well-established in several similar nearby
reservoirs), and the resulting estimates of oil and gas volumes of these yet undiscovered
Prospective Resources. As poorer reservoir quality in peripheral areas was included in the
volumes of each successive resource category, the expected average value of porosity (or initial
water saturation) was decreased (or increased).
Table 4.1—Volumetric Assessment of Prospective Resources
(Prediscovery Stage): Estimates of Project PIIPs and EURs
Bases and Categories of Prospective Resources
Estimated Parameters
Units
Low Estimate
Best Estimate
High Estimate
Bulk Reservoir Pay Volume
Average Porosity
Pore Volume (PV)
Average Initial Water Saturation
Hydrocarbon Pore Volume (HCPV)
Average FVF (B oi)
M ac-ft
%
M ac-ft
%
M ac-ft
RB/STB
241.4
17%
41.0
18%
33.7
1.4
1,055.6
16%
168.9
19%
136.8
1.4
2,134.7
15%
320.2
20%
256.2
1.4
Oil Initially In-Place (OIIP)
Recovery Factor
Recoverable Oil(EUR)*
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Gas Initially In-Place (OGIP)
MMSTB1
% OIIP
MMSTB
scf/STB
Btu/scf
Bscf
186.5
35%
65.3
500
1,200
93.2
758.1
40%
303.2
500
1,200
379.0
1,419.5
45%
638.8
500
1,200
709.8
Recoverable Raw Gas (EUR)*
Bscf
32.6
151.6
319.4
6.8
31.4
66.1
2
MMBOE
3
1
Calculated by using the conversion factor of 7,758 bbl/acre-ft.
2
Under Peripheral Water Injection, already well-established in several nearby analog reservoirs and projects.
3
Calculated using an average conversion factor of 5.8 MMBtu per BOE.
* Estimated Oil and Gas Prospective Resources categories of Low, Best and High, respectively.
Post-Discovery Stage. The wildcat well was drilled and encountered a significant oil column
sufficient to declare a “discovery.” The geologic model was updated as Fig. 4.3 for the
discovered structure and well-based reservoir data with an estimated average NTG ratio of 0.75,
translating into a net pay of 89 ft.
The discovery Well 1 flowed oil, but insufficient pressure data were retrieved and gradient
analysis could not be performed, thus the low estimate of technically recoverable volume could
not be allocated below the lowest known hydrocarbon (LKH) at 6,155 ft subsea.
Although preliminary economics of a proposed development plan were encouraging, there
was still significant uncertainty, and the chance of its commercial development was estimated to
be only 60%. Therefore, estimates of technically recoverable volumes of this discovered
accumulation could only be reclassified as Contingent Resources. Even though the chance of an
updip gas cap above the highest known hydrocarbon (HKH) could not be ruled out, the majority
of analog reservoirs are undersaturated and hence, for simplicity, it was neglected while
developing these maps and until the detailed pressure/volume/temperature (PVT) analysis and
pressure-gradient data became available for confirmation.
High estimate (3C) assumed that the structure is full with oil to its spill point, and alternative
geologic maps indicated a larger closure and higher recovery efficiency. Lacking any further
control than the LKH, which defined the 1C limit, regional analogs supported the forecast that
Assessment of Petroleum Resources Using Deterministic Procedures
43
the vertical limit for the best estimate (2C) could be set at an intermediate depth of 6,283 ft
subsea and that the recovery efficiency is slightly above that assumed for the 1C scenario. Based
on the discovery well structure and log data, and an average oil gravity of 32o API measured
from the oil samples collected, the volumetric weighted average reservoir parameters were
revised accordingly, but recovery factors were kept the same at this stage.
Subsea
Depth (ft)
-6000 A
1
A'
-6100
Low Estimate (1C)
-6155'
-6200
Best Estimate (2C)
-6300
-6400
-6283'
High Estimate (3C)
-6410'
Spill Point
-6500
(a) Structural Cross Section AA’ (not to scale horizontally)
(b) Net Pay Isochore Map for Low Estimate (1C)
Discovery Well
1
89’
(c) Net Pay Isochore Map for Best Estimate (2C)
Contour Lines (ft)
1
Discovery Well
89’
(d) Net Pay Isochore Map for High Estimate (3C)
1
Discovery Well
89’
0
1
2
Kilometers
Fig. 4.3—Volumetric assessment of Contingent Resources: post-discovery stage [Wang (2010)]
Guidelines for Application of the Petroleum Resources Management System
44
Table 4.2 documents average reservoir rock and fluid properties, and resulting estimates of
relevant volumes of oil and gas for Contingent Resources. Note again that the “average” porosity
is lower in the 2C and 3C scenarios, reflecting decreasing porosity (and increasing water
saturation) in the peripheral areas included in the higher estimates of bulk reservoir pay volume.
Table 4.2—Volumetric Assessment of Contingent Resources
(Post-Discovery Stage): Estimates of Project PIIPs and EURs
Bases and Categories of Contingent Resources
Estimated Parameters
Units
Low Estimate
Best Estimate
High Estimate
Bulk Reservoir Pay Volume
Average Porosity
Pore Volume (PV)
Average Initial Water Saturation
Hydrocarbon Pore Volume (HCPV)
Average FVF (Boi)
M ac-ft
%
M ac-ft
%
M ac-ft
RB/STB
448.4
19.1%
85.6
14.5%
73.2
1.4
1,258.7
18.9%
237.9
14.8%
202.7
1.4
2,287.1
18.7%
427.7
15.2%
362.7
1.4
Oil Initially In-Place (OIIP)
MMSTB 1
% OIIP
MMSTB
scf/STB
Btu/scf
Bscf
Bscf
MMBOE3
405.8
35%
142.0
500
1,200
202.9
71.0
14.7
1,123.2
40%
449.3
500
1,200
561.6
224.6
46.5
2,009.8
45%
904.4
500
1,200
1,004.9
452.2
93.6
Recovery Factor2
Recoverable Oil(EUR)*
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Gas Initially In-Place (OGIP)
Recoverable Raw Gas (EUR)*
1
Calculated by using the conversion factor of 7,758 bbl/acre-ft.
Under Peripheral Water Injection, already well-established in several nearby analog reservoirs and projects.
3
Calculated using an average conversion factor of 5.8 MMBtu per BOE.
* Estimated Oil and Gas Contingent Resources categories of 1C, 2C and 3C, respectively.
2
At this stage, remaining uncertainty in the project’s commercial development was still
considered significant, and without the benefit of additional data (e.g., from further delineation,
bottomhole PVT samples, pressure-gradient and definitive production tests and associated
analysis), the owners were not willing to commit funds to a development project. To better
ascertain its commercial potential, an appraisal program designed to further evaluate the
discovery was deemed necessary.
Appraisal (or Delineation) Stage. An appraisal program was designed and implemented,
including (1) drilling of two additional wells with well testing and PVT analysis, and (2)
acquisition and interpretation of 3D seismic data. It took two years to execute the Appraisal
Program and complete the necessary analyses and interpretations.
Both Wells 2 and 3 penetrated and established new LKH depths, thereby extending the base
for the low estimate to 6,240 ft subsea. PVT analysis of bottomhole fluid samples showed that oil
was undersaturated. Undersaturated oil, supported also by pressure-gradient measurements,
eliminated the potential for a gas cap. It was further determined that
• The carbonate reservoir had an initial pressure (pi) of 3,230 psia, temperature of 200oF,
estimated average porosity of 18.7%, 15% initial water saturation, and 400 md permeability.
• The wells tested at rates (rounded to the lower 100) ranging from 2,500 to 5,000 BOPD, with
an average stabilized oil rate of 3,500 BOPD, oil gravity of 33oAPI, and viscosity of 0.7 cp.
• The reservoir had a bubblepoint pressure (pb) of 1,930 psia, initial solution gas/oil ratio
(GOR), or Rsi, of 550 scf/STB, and initial (Boi) and bubblepoint (Bob) FVFs of 1.33 RB/STB
and 1.35 RB/STB, respectively.
Assessment of Petroleum Resources Using Deterministic Procedures
45
Fig. 4.4 illustrates the revision made for additional data obtained from this appraisal
program. Based on the net reservoir distribution (via NTG ratios) in Well 1 (0.75), Well 2 (0.70),
and Well 3 (0.55), a NTG surface was generated and used to develop the map views (Figs 4.4b to
4.4d), illustrating the interpreted areal extent and net reservoir volume for each reserves
category.
Subsea
Depth (ft)
-6000 A
3
1
2
A'
-6100
-6200
-6300
-6400
Low Estimate (1P)
-6240'
Best Estimate (2P)
-6325'
High Estimate (3P)
-6410'
Spill Point
-6500
(a) Structural Cross Section AA’ (not to scale horizontally)
(b) Net Pay Isochore Map for Low Estimate,1P
2
84’
1
3
89’
65’
(c) Net Pay Isochore Map for Best Estimate, 2P
2
84’
Contour Lines (ft)
Oil Well
1
3
89’
65’
(d) Net Pay Isochore Map for High Estimate, 3P
2
84’
1
3
89’
65’
0
1
2
Kilometers
Fig. 4.4—Volumetric assessment of Reserves: appraisal stage [Wang (2010)]
Guidelines for Application of the Petroleum Resources Management System
46
An initial development program including pressure maintenance by means of peripheral
water injection was applied. This recovery method is a well-established and common depletion
method in several analog reservoirs and projects. With favorable project economics, the owners
committed investment funds to the project and gave approval to proceed with the next
development stage. No market, legal, or environmental contingencies were foreseen. Therefore,
consistent with PRMS, the new estimates of recoverable quantities from the applied project are
now reclassified as Reserves.
1P Reserves were assigned to the PIIP volume above the LKH established at 6,240 ft subsea.
Although seismic amplitude analysis indicated potential extending below the LKH, it was
insufficient to support extending Proved Reserves below this LKH depth. 2P Reserves were
allocated to the total PIIP volume above 6,325 ft subsea. In the absence of original oil/water
contact (OWC), 3P Reserves were assigned to the total PIIP volume above 6,410 ft subsea (or
spill point). Three wells and 3D seismic data provided increased structural control. Based on
similar regional analogs, there was reasonable potential that the structure was filled with oil to
the spill point.
It may be important to note that in deterministic analysis, both scenario and incremental
approaches (allowed by PRMS) generate the materially equivalent estimates. Based on the bulk
reservoir pay volume associated with each incremental category obtained from the difference
between the relevant maps, the incremental approach can also be used to directly calculate the
Proved, Probable and Possible Reserves. Estimates should be very consistent with those obtained
from the cumulative (scenario) approach provided that care is taken in estimating reasonable
average values of porosity and initial water saturation for each incremental volume to yield
correct the PIIPs. For simplicity in presentation, incremental analyses are not included here.
Table 4.3 documents the revised average reservoir rock and fluid properties, and resulting
estimates of relevant oil and gas volumes for each reserves category. The project is located close
to existing infrastructure; therefore, an overall development plan was prepared for immediate
implementation.
Table 4.3 — Volumetric Assessment of Reserves (Appraisal Stage):
Estimates of Project PIIPs and EURs
Bases and Reserves Categories
Estimated Parameters
Units
Low Estimate
Best Estimate
High Estimate
Bulk Reservoir Pay Volume
Average Porosity
Pore Volume (PV)
Average Initial Water Saturation
Hydrocarbon Pore Volume (HCPV)
Average FVF (Boi)
M ac-ft
%
M ac-ft
%
M ac-ft
RB/STB
821.0
18.9%
155.2
14.8%
132.2
1.330
1,370.8
18.7%
256.3
15.0%
217.9
1.330
1,917.9
18.5%
354.8
15.3%
300.5
1.330
Oil Initially In-Place (OIIP)
Recovery Factor2
Recoverable Oil(EUR)*
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Original Gas In-Place (GIIP)
Recoverable Raw Gas (EUR)*
MMSTB1
% OIIP
MMSTB
scf/STB
Btu/scf
Bscf
Bscf
MMBOE3
771.2
35%
269.9
550
1,200
424.1
148.4
30.7
1,271.0
40%
508.4
550
1,200
699.0
279.6
57.9
1,753.0
45%
788.8
550
1,200
964.1
433.9
89.8
Calculated by using the conversion factor of 7,758 bbl/acre-ft.
Under Peripheral Water Injection, already well-established in several nearby analog reservoirs and projects.
Calculated using an average conversion factor of 5.8 MMBtu per BOE.
* Estimated Oil and Gas Reserves categories of 1P, 2P and 3P, respectively.
Assessment of Petroleum Resources Using Deterministic Procedures
47
The total area, about 60 1-km WSUs, defined as Proved by three wells in this example
reflects an extremely high confidence in the lateral continuity of the productive reservoir. Such
continuity of a high-quality reservoir with average permeability of 400 md was also supported by
numerous surrounding analogs. Thus, it meets PRMS criteria for reasonable certainty. 1P
reserves are considered Proved Undeveloped (PUD) status for now. However, based on a well
drainage area of 1 km2 (or about 250 acres) derived from single-well simulation studies, at least
three 1-km WSUs (out of a total Proved of about 60) penetrated by these three productive wells
represent a portion of approximately 5% of the total Proved volume (or about 38.5 MMSTB of
the OIIP), which can be carried under Proved Developed (PD) status immediately after the
installation of necessary equipment.
Initial Development (or Exploitation) Stage. Similar to well-established development and
production practices in several nearby analogs producing from the same reservoir, a single
recovery project integrating the primary and secondary waterflood development programs was
recommended and approved for immediate implementation. The project was designed with an
initial plateau production rate of 75,000 BOPD targeting an annual depletion of 5.4% of 2P
reserves of 508.4 MMSTB (from Table 4.3) and supported by peripheral water injection with an
injection rate of 100,000 BWPD. Pressure maintenance by peripheral water injection had been
established to be a very effective depletion method in several nearby analog projects where the
water injected into a partially active edgewater aquifer displaces the oil column updip thereby
achieving oil recoveries, in some cases, exceeding 60% OIIP.
Based on an assumed conservative average well production rate that may vary between 2,500
and 3,000 BOPD, the initial development project required a total of 34 producers (including the
three existing productive wells) to establish a balanced withdrawal fieldwide. The time line
accounted for an operating factor in production rate considering annual downtime for preventive
maintenance of surface facilities, including inspection, repairs, and testing. The project also
required eight water supply wells from a local shallow aquifer and 19 peripheral water injectors
(to inject produced plus externally supplied water) to maintain reservoir pressure and to provide
balanced updip displacement. The project included pertinent surface production and injection
facilities and associated pipelines. Based on this well-defined development plan, the production
profile and required initial capital investment (for drilling and well completions, well flowlines,
surface production and injection facilities and pipelines), and future capital (for future wells and
flowlines) and operating expenditures required during the project’s economic life, the recovery
project’s economic viability was reconfirmed. The approval was given to include the project in
the company’s capital budget.
The project development took 3 years to complete and bring on stream in the fourth year (or
just 3 years after appraisal and 5 years after the initial discovery). First, a total of 8 water supply
wells (from a shallow and large regional aquifer already proven to be productive and supporting
water injection in several other fields) and 34 additional oil wells in this example oil reservoir
were drilled that included three dry holes (Wells 4, 7, and 15). It was followed by drilling the 19
water injectors at the periphery. The example oil reservoir was significantly delineated by these
56 wells. The original OWC was established at 6,340 ft subsea by well logs and supported by
analysis of pressure-gradient data.
Fig. 4.5a represents the cross section based on the revised interpretation. Wells illustrated by
dashed lines on this cross section are projected. The net pay isochore map (Fig. 4.5b) was
developed with NTG ratios ranging from 0.3 to 0.9 (with majority greater than 0.7) obtained
Guidelines for Application of the Petroleum Resources Management System
48
from the well logs and available cores, and supported further by full production tests conducted
in six more strategically placed wells. Measured stabilized well rates and estimated reservoir
permeability from buildup tests had ranged from 1,500 to 5,000 BOPD, and 150 to 500 md,
respectively, with overall reservoir averages estimated to be about 2,500 BOPD and 350 md.
The reservoir parameters entered for each polygon are the volume-weighted averages.
Because several wells penetrated the original OWC at 6,340 ft subsea, the entire enclosure was
judged to represent a single most likely OIIP estimate of about 1,430 MMSTB. Based on similar
nearby analog reservoirs with minor changes in reservoir structure and average reservoir
parameters (and their distributions), developing separate OIIP for each reserves category was
considered unwarranted by analysts at the time. It is recognized that this may not be typical of
other developments where significant uncertainty associated with in-place volumes persists into
late stages of development. In all cases, uncertainty should decrease over time as the amount and
quality of data improves, including periodic updates of PIIPs using performance-based methods.
It may be important to note that Fig. 4.5b depicts the well requirements (in black dots) for
initial development only, representing just over one-third of the total WSUs available. Additional
drilling of oil producers (and a few water injectors) was carried out during the later stages of the
Subsea
Depth (ft)
-6000
13 10
15 3 9 1 14
4 8
6 11 2 12 5 7
A
A'
-6100
-6200
-6300
OWC
-6340'
-6400
-6500
(a) Structural Cross Section AA’ (not to scale horizontally)
Oil Wells: Initial (34)
25
Next 8 yrs (16)
Dry Holes (3)
26
15
Water Injectors (19)
27
Contour Lines (ft)
24
14
34
35
6
5
36
28
33
1
13
29
22
37
3
8
12
32
30
10
31
11
9
16
4
7
23
2
17
18
21
20
19
0=OOWC
0
1
2
Kilometers
(b) Net pay isochore map and depiction of wells for initial development plan and beyond
Fig. 4.5—Volumetric assessment of Reserves (initial development stage).
Assessment of Petroleum Resources Using Deterministic Procedures
49
production phase (e.g., 16 producers, not numbered but shown in hollow circles were actually
drilled during the first 8 years of production) to extend the plateau production rate, to help
improve volumetric sweep, and to better manage the production decline. More wells were drilled
during the later stages to fully develop the reservoir.
As compared with its appraisal stage, the reservoir was significantly delineated, and analyses
of an additional 31 productive oil well logs and tests indicated a better average reservoir quality
than that seen in analogs. Thus, the recovery efficiencies for all reserves categories were
increased modestly by 5% OIIP from their respective levels in the appraisal phase, bringing the
high estimate to 50% OIIP. However, these estimates would be revised in future re-assessment
studies as additional production data was obtained and new wells were drilled.
Reservoir average rock and PVT data were revised and documented in Table 4.4. With the
revised OIIP (1,429.6 MMSTB) and increased reserves, the initial plateau production rate of
75,000 BOPD represented an annual depletion rate of only 4.3% of 2P reserves.
Analysis of six additional well tests and several single-well simulation studies have further
supported the validity of 1 km2 (or about 250 acres) average well spacing. There were
approximately 98 1-sq-mile WSUs in the area described by the original OWC. Although wells
were not necessarily drilled in the center of each WSU (Fig. 4.5b), about 35 WSUs (or about
36% of total) may be allocated to the Proved Developed (PD) reserves status. Therefore, under
PRMS guidelines and as described in the Appraisal Stage earlier, based on the developed OIIP
portion of 514.5 MMSTB (refer to Table 4.4), 25.7 MMSTB (= 514.7 x 0.05) oil and 14.7 Bscf
raw gas from the recoverable volumes assigned to both 2P and 3P can be allocated to Developed
status in Table 4.4, but were not shown separately here to keep the table as simple as possible.
Table 4.4—Volumetric Assessment of Reserves
(Initial Development Stage): Estimates of Project PIIPs and EURs
Bases and Reserves by Category and Status
Estimated Parameters
Proved Status*
Low
Estimate
Proved
Proved
Developed Undeveloped
(PUD)
(PD)
Bulk Reservoir Pay Volume
Average Porosity
Pore Volume (PV)
Average Initial Water Saturation
Hydrocarbon Pore Volume (HCPV)
Average FVF (Boi)
M ac-ft
%
M ac-ft
%
M ac-ft
RB/STB
1,523.3
19%
289.4
15%
246.0
1.335
548.4
19.0%
104.2
15.0%
88.6
1.335
974.9
19.0%
185.2
15.0%
157.4
1.335
Original Oil In-Place (OIIP)
MMSTB1
% OIIP
MMSTB
scf/STB
Btu/scf
Bscf
Bscf
MMBOE3
1,429.6
40%
571.9
570
1,350
814.9
326.0
75.9
514.7
40%
205.9
570
1,350
293.4
117.3
27.3
915.0
40%
366.0
570
1,350
521.5
208.6
48.6
Recovery Factor2
Recoverable Oil(EUR)*
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Original Gas In-Place (GIIP)
Recoverable Raw Gas (EUR)*
1
Units
Proved
Best
Estimate
High
Estimate
1,429.6
45%
643.3
570
1,350
814.9
366.7
85.4
1,429.6
50%
714.8
570
1,350
814.9
407.4
94.8
Calculated by using the conversion factor of 7,758 bbl/acre-ft.
Under Peripheral Water Injection, already well-established in several nearby analog reservoirs and projects.
3
Calculated using an average conversion factor of 5.8 MMBtu per BOE.
* Estimated Oil and Gas Reserves categories of 1P, 2P and 3P, respectively.
2
Guidelines for Application of the Petroleum Resources Management System
50
Finally, the example oil project’s EOR potential is supported by the results of a miscible CO2
pilot project from an analog reservoir with incremental recovery of 20% OIIP. Based on the
same single project OIIP estimate, three categories of Contingent Resources were assigned for
this potential project using conservative incremental recovery efficiencies of 5%, 10%, and 15%
OIIP and summarized in Table 4.5.
Table 4.5—Volumetric Assessment of Contingent Resources
(Initial Development Stage): Estimates of Project EURs
Bases and Categories of Contingent Resources
Estimated Parameters
Original Oil In-Place (OIIP)
Recovery Factor1
Recoverable Oil*
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Original Gas In-Place (GIIP)
Recoverable Raw Gas*
1
Units
Low Estimate
Best Estimate
High Estimate
1,429.6
5%
71.5
570
1,350
814.9
40.7
9.5
1,429.6
10%
143.0
570
1,350
814.9
81.5
19.0
1,429.6
15%
214.4
570
1,350
814.9
122.2
28.5
MMSTB
% OIIP
MMSTB
scf/STB
Btu/scf
Bscf
Bscf
MMBOE2
Under a CO2 Miscible Flood based on the results of an already implemented nearby anolog CO2 Pilot Project.
2
Calculated using an average conversion factor of 5.8 MMBtu per BOE.
* Estimated Oil and Gas Contingent Resources categories of 1C, 2C and 3C, respectively.
c.) Use of Geocellular Models in Estimating Petroleum In-Place Volumes. While not
illustrated in this particular example oil recovery project, given the 3D seismic and early well
control, conventional geologic mapping is often supplemented by 3D geologic modeling.
Advances in computer technology have facilitated the widespread applications in building
multimillion-cell digital geocellular models populated cell by cell with the static geological,
geophysical, petrophysical, and engineering data characterizing the subsurface reservoir structure
in 3D, similar to the depiction in Fig. 4.6.
k , h, φ, Swi , B hi , etc.
j= n
A j h jφ j (Shc ) j
j=1
( B hi ) j
OHIP = ∑
Wells
Cell
3-D Geological Model
Aquifer
Fig. 4.6—3D multicell geological model [adapted from PS-CIM (2004)]
Assessment of Petroleum Resources Using Deterministic Procedures
51
In a gridded mapping process, the parameters in the original hydrocarbon in-place (OHIP)
equation change from cell to cell, and the total OHIP is obtained by the sum of the individual
values assigned to, calculated for, and/or matched for each cell. Based on early well
performance, modifications to the development program including supplemental secondary and
enhanced recovery projects can be designed using streamline and/or finite-difference simulation
models with such multimillion-cell reservoir characterization models, including several cases of
“what-if” scenarios represented by different plausible realizations. However, refinement and
verification of these large geocellular models with actual analogs and thus the degree of certainty
in the resulting estimates to a large extent is dependent on both the quantity and quality of
geoscience, engineering, and, more importantly, the performance data.
4.2.3 Performance-Based Methods. As illustrated in Fig. 4.1, the Production Phase can be
divided into three producing stages (or periods) of Early Time (IIa), Late Time (IIb), and Decline
(IIc), which show the increasing project maturity and changing of applicable resources
assessment methods over time. Depending on the amount and quality of historical pressure,
production and other reservoir performance data available, a combination of reservoir
simulation, material balance, and production performance trend (PPT) analysis (or decline curve
analysis) can be used not only to directly estimate the recoverable petroleum, but also the
petroleum in-place quantities (by the first two methods only), and thereby provide a useful
second check and validation of estimates obtained earlier by volumetric methods.
Material Balance Methods. Material balance methods are part of performance-based
dynamic analyses. The performance data include production and injection profiles, volumeweighted average reservoir pressures, and reservoir-specific relevant fluid and rock properties
(co, cg and cw; Bo, Bg, Rs, Rv, and Bw) all as a function of reservoir pressure and temperature.
Independent of the volumetric methods, the material balance methods can be used to directly and
simultaneously estimate PIIP, the size of its gas-cap (m), or its in-place volume [gas cap initially
in place (GCIIP)], and/or the water influx (We). The results of material balance analysis are
considered more reliable with longer performance histories and high-quality production data,
both measured and interpreted. A well-established and reasonable assumption is that use of the
material balance analysis to estimate PIIP is often considered valid if the cumulative production
exceeds 10% PIIP providing the development of the accumulation is such that the pressures used
in the analysis represent an average over the entire reservoir. Uncertainty in the estimate is
expected to decrease over time as historical production performance data cover at least the early
production period (IIa) and beyond.
Application to Example Oil Project in Its Early-Production Stage. a.)Technical Principles.
Technical principles and definition of terms involved in developing the conventional material
balance equation (MBE) applicable to any oil and gas reservoir (i.e., black or volatile oil and
retrograde or nonretrograde gas) and applications may be found in Walsh and Lake (2003), and
Towler (2002). Modern flowing and dynamic material balance analyses developed by Mattar and
McNeil (1998) and Mattar and Anderson (2005) may also be used.
The example oil project represents a black-oil reservoir, initially undersaturated (i.e., no gas
cap) with partially active water influx, which was developed by peripheral down-dip water
injection to supplement reservoir energy and to help maintain a constant reservoir pressure 100
to 200 psia above the bubblepoint pressure. Furthermore, above the bubblepoint (Rs = Rsi = Rp),
Guidelines for Application of the Petroleum Resources Management System
52
all gas produced at the surface would be dissolved in the oil. The straight line Havlena-Odehtype (Havlena and Odeh 1963 and 1964) MBE for this particular case can be written as
F p = N [(Bo - Boi ) + (cw S wi + c f ) /S oi ) ΔP Boi ] + (We + Winj Bw )
(4.2)
It can be further simplified and re-written in terms of effective reservoir compressibility (ce) as
follows:
F p = N (Boi ce ΔP ) + (W e + Winj B w )
(4.3)
where the variables and terms given are defined by the following relationships:
1) Left side of Eq. 4.3 represents cumulative net reservoir withdrawal (Fp) defined by
F p = N p B oi + W p B w
(4.3a)
2) Right side of Eq. 4.3 represents cumulative net reservoir expansion terms (Ep) and the water
influx (We), which is given in terms of the van Everdingen and Hurst (1949) unsteady
solution by
k 1
We = U
∑ ΔP j +1WD (rD , Δt Dj )
(4.3b)
j=0
where j = 0 indicates initial reservoir conditions when Pi = Po and k = 1, 2,…., n and n is the
number of time intervals for which the historical pressure, production, and injection data are
available.
The effective, saturation-weighted compressibility of the reservoir system (oil, water, and the
formation or reservoir rock pore volume) in Eq. 4.3 is defined by
ce = ( co Soi + cw S wi + c f ) /Soi
(4.3c)
Eq. 4.3 can also be re-arranged as
F p /(Boi ce ΔP ) = N + [We + Winj Bw ]/(Boi ce ΔP )
(4.4)
This MBE represents reservoir depletion under a combined waterdrive (i.e., water influx and/or
down-dip water injection into the aquifer) that is effective and strong enough to maintain average
reservoir pressure above the bubblepoint pressure. Because water is injected into the aquifer at
the periphery, it is treated as part of the water aquifer irrespective of how much of the water
actually enters the oil zone and helps displace oil or how much of it enters the aquifer.
Eq. 4.4 suggests that a plot of the left-hand side vs. the second term of the right-hand side
should yield a straight line of unit slope intercepting the ordinate at N (or OIIP). Data necessary
for this plot can be generated at each timestep as follows: At any time period with an
appropriate ΔP , (1) the Fp, ce data can be calculated using the relevant relationships and
measured production and injection data, (2) the unsteady-state water influx theory of van
Everdingen and Hurst (1949) may be used to estimate dimensionless influx rates (WD), and (3)
Eq. 4.3b can be used to calculate water influx (We).
Assessment of Petroleum Resources Using Deterministic Procedures
53
b.) Application. The oil reservoir evaluated in this application example is a prolific carbonate
reservoir with undersaturated oil, developed and producing with very effective down-dip water
injection that has maintained the reservoir pressure over the bubblepoint. An additional 16 new
oil producers and one water injector were also drilled during this 8-year production period
(bringing the total to 50 and 20 wells, respectively) to maintain plateau rate and help improve
overall recovery efficiency. The project produced 220.8 MMSTB of oil (15.4% OIIP of 1,429.6
MMSTB estimated and reported earlier in Table 4.4), 126 Bscf of solution gas and 80 MMSTB
of water and injected 385 MMSTB of produced and supply water into the aquifer below the
original OWC.
Based on the average reservoir pressure observed, production and injection performance data
recorded over an 8-year period (the first-year data were erroneous, out of scale, and excluded),
the terms in Eq. 4.4 were calculated and plotted in Fig. 4.7.
Assessment OIIP for the Unsaturated Example Oil Reservoir
12,000
Estimate
High
Best
Low
F/(Boi Ce ΔP), MMSTB
10,000
N (MMSTB)
2,100
1,600
1,300
8
6
5
8,000
2
3
7
4
6,000
For rD= (rA/rR) = 5
2, 3, 4 .... Time (years)
4,000
2,000
0
0
2,000
4,000
6,000
8,000
10,000
12,000
(We + Winj Bw)/(Boi Ce ΔP), MMSTB
Fig. 4.7—Assessment of OIIP by material balance methods (early-production stage).
With the variations shown in the plotted data, it was possible to draw three parallel straight
lines with a unit slope confirming the correct value of the dimensionless radius, rD= 5 (defined as
a ratio of the aquifer radius and reservoir radius) and bracketing the degree of uncertainty in the
measured and interpreted data and thus the resulting estimates of in-place volumes. These
minimum, most likely, and maximum interpretations of OIIP (or N) values of 1,300, 1,600, and
2,000 MMSTB were assumed to represent the low, best, and high estimates, respectively. These
OIIP estimates were judged to be a valid basis for assigning 1P, 2P, and 3P Reserves categories
because (1) the project produced more than 10% OIIP (or about 17% of low and 14% of best
OIIP), (2) a reasonably good match was obtained in Fig. 4.7 and deviations are accounted for,
and (3) it was supported by a new volumetric in-place estimate of 1,567 MMSTB reported by
analysts updating the old estimate (versus 1,430 MMSTB after completion of initial
development) by incorporating additional data from 16 new producers drilled over this 8 years of
production.
Over the past 8 years, the ongoing peripheral waterflood project confirmed similar
performance to the analogs nearby and a second CO2 pilot was also implemented showing
similar initial performance to the first one already completed. However, to further ensure
Guidelines for Application of the Petroleum Resources Management System
54
reasonable confidence in the estimates, the recovery efficiencies were not changed at the time
and kept the same as the initial development stage 8 years earlier. Based on these low, best, and
high estimates OIIPs, the respective EURs and Reserves (under the ongoing Peripheral
Waterflood Project) and the Contingent Resources (under a proposed CO2 Miscible Project) were
calculated and summarized in Table 4.6.
Table 4.6 —Assessment using Material Balance Methods (Early-Production Stage):
Estimates of Project PIIPs, EURs, Reserves and Contingent Resources
Bases and Estimates by Reserves and Resources Category
Measured and Estimated Parameters
Cumulative Production
- Oil
- Raw Gas
Original Oil In-Place (OIIP)
Recovery Factor1
Recoverable Oil (EUR)*
- Original
- Remaining*
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Original Gas In-Place (GIIP)
Recoverable Raw Gas (EUR) - Original
- Remaining*
Units
Low Estimate
Best Estimate
High Estimate
MMSTB
% OIIP
Bscf
MMSTB
% OIIP
MMSTB
MMSTB
scf/STB
Btu/scf
Bscf
Bscf
MMBOE2
Bscf
MMBOE2
220.8
17.0%
125.9
1,300
40%
520.0
299.2
570
1,350
741.0
296.4
69.0
170.5
39.7
220.8
13.8%
125.9
1,600
45%
720.0
499.2
570
1,350
912.0
410.4
95.5
284.5
66.2
220.8
11.0%
125.9
2,000
50%
1,000.0
779.2
570
1,350
1,140.0
570.0
132.7
444.1
103.4
Units
Low Estimate
Best Estimate
High Estimate
% OIIP
MMSTB
Bscf
MMBOE2
5%
65.0
37.1
8.6
10%
160.0
91.2
21.2
15%
300.0
171.0
39.8
Basis and Categories of Contingent Resources
Recovery Factor3
Recoverable Oil*
Recoverable Raw Gas*
1
2
Under Peripheral Water Injection Scheme that maintains reservoir pressure above the bubblepoint.
Calculated using an average conversion factor of 5.8 MMBtu per BOE.
3
Under a CO2 Miscible Flood based on the results of one CO2 Pilot already implemented and a positive response from
a second pilot being applied in another nearby analog reservoir.
* Estimated categories of Oil and Gas Reserves of 1P, 2P and 3P and Contingent Resources of 1C, 2C and 3C.
Moreover, it was recommended that these estimates be updated in the future based on the
results of new re-assessment studies expected to incorporate data from additional wells drilled
and production performance data observed and recorded. It is recognized that this type of
traditional material balance analysis using analytical procedures has routinely been performed by
reservoir simulation studies, which are discussed next under Reservoir Simulation Methods.
Application to a Volumetric Gas Reservoir in Its Late Production and Early Decline. a)
Technical Principles. In volumetric gas reservoirs there is no (or insignificant) aquifer water
influx, and the volume of initial HCPV will not significantly decrease and remain constant
during reservoir pressure depletion. Therefore, with no adjoining aquifer or water influx (We =
0), no water production (Wp = 0), and injection of gas (Ginj = 0), the generalized conventional
MBE for a volumetric gas reservoir reduces to (Lee and Wattenbarger 1996):
Gp Bg= G (Eg + Bgi E w + Bgi E f)
(4.5)
Assessment of Petroleum Resources Using Deterministic Procedures
55
Except for the special case of abnormally pressured gas reservoirs, relative to significantly high
gas compressibility (or cg approximately equal to the inverse of reservoir pressure), the formation
water (Ew) and formation or pore-volume compression (Ef) terms can be neglected because Eg
>>> (Bgi E w+ BgiE f), and the Eq. 4.5 will further reduce to:
Gp Bg = G Eg = G (Bg-Bgi)
(4.6)
and the gas formation factor (Bg) can be calculated using
Bg (RB/scf) = 5.0435(10-3) zT/p [or 2.8269 (10-2) zT/p in Rcf/scf]
(4.6a)
where standard surface pressure (psc) and temperature (Tsc) conditions are 14.7 psia and 60 oF.
It is common practice to express this relationship in terms of average reservoir pressure by
combining Eqs. 4.6 and 4.6a and rearranging to yield this well-known material balance equation
applicable only to volumetric gas reservoirs:
(p/z) = (pi/zi)-[(pi/zi)/G] Gp ,
(4.7)
where
pi, p = average reservoir pressure (psia) at reservoir datum and “i” stands for initial,
T = average reservoir temperature at reservoir datum (oR),
zi and z = gas compressibility factors evaluated at pi and T and any p and T, respectively,
G = GIIP (scf), and
Gp = cumulative gas production (scf) at any reservoir pressure (p).
Eq. 4.7 simply asserts that in volumetric gas reservoirs, the gas production, and therefore the
ultimate recovery under natural pressure depletion is a direct function of the expansion of the
free gas initially in-place. The lower the economic limit (or abandonment pressure), the higher
the EUR. Furthermore, Eq. 4.7 suggests that a plot of (p/z) vs. Gp should yield a straight line with
an intercept (pi/zi) and a slope of [-(pi/zi)/G] from which the GIIP = G and EUR at the economic
limit (p/z) can be estimated.
b.) Application to Example Gas Project. A deep carbonate, normally-pressured and
volumetric reservoir with wet gas has been on production for the past 22 years and produced
about 316 Bscf of raw natural gas and 9 MMSTB of condensate. Based on several analog
onshore projects producing from the same formation in different nearby gas fields, it has been
determined that the gas exhibits borderline retrograde behavior. However, several laboratory
tests and compositional model study results verified that condensate dropout in the reservoir
during depletion drive below dewpoint pressure is not significant enough to justify gas cycling.
This minor loss has been reflected by the use of lower condensate recovery confirmed by the
analogs. The measured initial condensate gas ratio (CGRi) of 30 STB/MMscf was confirmed
during production above its reservoir dewpoint pressure, declining only to 27 STB/MMscf at a
reservoir pressure of about 5,500 psia (compared to 7,000 psia initial). The small loss was taken
into account by the use of a lower condensate recovery efficiency confirmed by the analogs.
Fig. 4.8 depicts the p/z vs. Gp performance plots for this example wet-gas reservoir. Because
of variations in the observed data under pressure depletion, it was possible to draw three different
straight lines bracketing the potential degree of uncertainty in the measurement and interpretation
of the historical data. These minimum, most likely, and maximum interpretations of GIIP
estimates from Fig. 4.8 were judged to represent the valid basis for assigning different reserves
categories of 1P, 2P, and 3P, respectively, based on an estimated (p/z) economic limit of 1,500
Guidelines for Application of the Petroleum Resources Management System
56
psia. The resulting implied volumetric recovery efficiency is calculated to be about 75 to 76% of
GIIP. Estimates are further supported by and considered reasonable because (1) the reservoir has
been established to be volumetric with nonretrograde gas, (2) it is fully delineated and developed
with a best estimate GIIP of 1,800 Bscf using volumetric methods, (3) it has already produced
316.2 Bscf, which is more than 17.6% of this volumetric GIIP or 21.1% of the low GIIP estimate
from Fig. 4.8, and (4) the project economics based on these three different scenarios are all
determined to be viable with discounted cash flow rates of return (DCF-RORs) exceeding 20%.
The reserves status is considered Developed.
"( p/z ) vs . G p "
7,000
Performance Profiles for a Deep Volumetric Gas Reservoir
Reserves Category
Line
OGIP (Bscf)
1,500
1,710
1,940
1P (Low)
2P (Best)
3P (High)
6,000
EUR (Bscf)
RE (%OGIP)
1,130
1,300
1,475
75
76
76
5,000
4,000
● Historical Production
(psia)
EUR
3,000
p/z
2,000
OGIP
Economic Limit (p/z) = 1,500 psi
1,000
0
0
200
400
600
800
1000
1200
1400
1600
1800
2000
Cumulative Gas Production, G p (Bscf)
Fig. 4.8—Gas reserves assessment by material balance methods (Late-Production Stage).
Based on the initial condensate gas ratio (CGRi) of 30 STB/MMscf raw gas (with a gross
heating value of 1,100 Btu/scf) and a recovery factor of 60% original condensate in-place
(OCIP) from the nearby analog reservoirs, the in-place and reserves estimates for this gas
reservoir are summarized in Table 4.7. Note that the recoverable raw gas volumes (in terms of
both scf and therefore the barrels-oil-equivalent, BOE) summarized in Table 4.7 must be reduced
by approximately 20% for the surface loss to yield their residue sale gas equivalents or reserves
(EUR), consisting of 3.2% for the shrinkage of condensate reserves and 16.8% for the
subsequent processing to remove nonhydrocarbons (8.1%) and recovery of C2 plus NGLs
(8.7%). For more detail, readers should refer to Chapters 9 and 10 on production measurements,
reporting, and entitlement issues.
Assessment of Petroleum Resources Using Deterministic Procedures
57
Table 4-7—Gas Reserves Assessment by Material Balance Methods
(Late-Production Stage): Estimates of Project GIIPs, CIIPs, EURs and Reserves
Bases and Estimates by Reserves Category
Measured and Estimated Parameters
- Raw Gas
Cumulative Production
- Condensate
Gas Initially In-Place (GIIP)1
Gross-Heating Value of Raw Gas
1
Recoverable Raw Gas (EUR)
Units
Low Estimate
Best Estimate
High Estimate
Bscf
316.2
316.2
316.2
% GIIP
21.1%
18.5%
16.3%
MMSTB
9.4
9.4
9.4
Bscf
1,500
1,710
1,940
Btu/scf
1,100
1,100
1,100
- Original
Bscf
1,130
1,300
1,475
MMBOE2
Bscf
214.3
246.6
279.7
- Remaining*
813.8
983.8
1,158.8
MMBOE2
% GIIP
154.3
186.6
219.8
75%
76%
76%
Implied Recovery Factor
Initial Condensate-Gas Ratio (CGRi)
STB/MMscf
Condensate Initially In-Place (CIIP)
MMSTB
Condensate Recovery Factor3
Recoverable Condensate (EUR) - Original
% CIIP
60%
60%
60%
MMSTB
27.0
30.8
34.9
MMSTB
17.6
21.4
25.5
- Remaining*
30
30
30
45.0
51.3
58.2
1
Estimated directly from Figure 4-8 based on (P/Z) values of 0 and 1,500 psia (economic limit ), respectively.
2
Calculated using an average conversion factor of 5.8 MMBtu per BOE
3
Based on several nearby analog reservoirs and accounts for condensate dropout in the reservoir, if any.
* Estimated Gas and Condensate Reserves categories of 1P, 2P and 3P, respectively.
It is a common practice to determine whether “gas compression” is economically viable and
can be used to lower wellbore backpressure to help gas wells produce at lower average reservoir
abandonment pressures (or associated p/z economic limits) and thus provide additional reserves.
The wellbore backpressure is the sum of the backpressure imposed by the sales gas pipeline and
the pressure drops in the gas gathering system at the surface and the tubing string in the wellbore.
A gas well will stop flowing when the average reservoir pressure drops to and equals this
wellbore backpressure. This “no flow” average reservoir pressure and therefore its (p/z) value
does not necessarily represent the economic limit because the wellbore imposed backpressure
can be reduced by designing and installing an optimal gas compression facility (with an optimum
compression ratio) at the point of sales (or plant feed) to significantly reduce the sales gas
pipeline imposed backpressure.
The economic limit (p/z) of 1,500 psia for this example deep gas reservoir represents a point
where the value of production is just equal to the operating cost of producing the project under
pressure depletion without compression. It is a deep gas reservoir and although gas compression
is expected to reduce the economic (p/z) limit to as low as 1,000 psia, it is uneconomic because
the value of incremental gas reserves realizable is determined to be less than the capital and
operating costs of installing and running the compression facility. Thus the incremental volumes
associated with compression are considered as Contingent Resources (but not reported here)
pending future updates for cost reduction and/or higher gas prices
Guidelines for Application of the Petroleum Resources Management System
58
Reservoir Simulation Methods (RSM). a) Technical Principles. The body of scientific
knowledge on the development and use of integrated reservoir simulation models is extensive
and may be reviewed in many books, including Aziz and Settari (1979), Mattax and Dalton
(1990), Ertekin et al. (2001), Fanchi (2006), and many others. PS-CIM (2004) provides a brief
and concise review of the subject, including the different phases of a typical reservoir simulation
study.
A reservoir simulation model characterizes the reservoir by integrating the static geological
model (similar to that in Fig. 4.6) and the dynamic flow model populated with actual reservoir
performance data (pressures, tests, production rates, inter fluid-rock characteristic curves
characterized by the capillary and relative permeability curves, PVT data, etc). Moreover, the
results of integrated reservoir simulation models can be used with increased confidence as the
amount and quality of static geoscientific and dynamic reservoir performance data increase.
Reservoir simulation can be used during any production stage (or period) to directly estimate
both the original in-place and the recoverable quantities of petroleum or the EUR for any oil and
gas recovery project. Estimates may be derived for any petroleum recovery project under any
recovery method, including primary drive mechanisms, secondary pressure maintenance and
displacement schemes (crestal immiscible gas injection, and down-dip peripheral and pattern
waterfloods), and various potentially applicable EOR processes.
Developing a meaningful reservoir model capable of generating reliable results with reasonable
certainty requires a multidisciplinary team with appropriate technical skills and broad
experience. Once a reasonably good history match is obtained, the model can be used to predict
production and injection profiles, infill wells, well workovers, stimulation, and other
requirements according to specified prediction guidelines (related to drilling, well completions,
production engineering and reservoir management, including vertical flow and surface flow
systems) under various “what-if” conditions for reservoir development, production and
management strategies. Based on a comparative economic analysis, the optimum development
and producing strategy can be selected for implementation. Depending on the amount and quality
of performance data available, the projected cumulative production to the economic limit with
this optimum strategy should establish the most likely EUR.
Determination and assignment of different reserves categories, however, must be consistent
with PRMS definitions and therefore would depend on the degree of uncertainty the evaluator
determined to exist in these estimates. Irrespective of the assessment method, it is good practice
to consider the following two key points:
1. The degree of uncertainty in the estimates (or the range of outcomes) is expected to decrease
as the amount and quality of geoscience, engineering and production performance data
increase.
2. Compare the estimates obtained using several different methods (e.g., volumetric, material
balance, reservoir simulation and/or production performance trend analyses) and the analog
projects, if available, before booking reserves.
There are no published generally accepted rules, but several key observations can be made
regarding best practices employed in the assessment of petroleum in-place and recoverable
volumes using reservoir simulation studies. With limited data (geoscience and engineering), the
model is best suited to make sensitivity scenario projections to bracket what is possible around
the best estimate defined as the base case. The uncertainty in the range of estimates is expected
to be larger than those estimated using more data. As specified in the PRMS, based on the
Assessment of Petroleum Resources Using Deterministic Procedures
59
respective project economics and whether or not all project contingencies are met, resulting
estimates may be assigned to different categories of Reserves and/or Contingent Resources. As
the amount and quality of data increases, the range of estimates of in-place and recoverable
volumes obtained using these integrated reservoir simulation models (matched using long
observed production performance data) will decrease. In actual practice, one may have the
following two extreme cases in which to assess and categorize the estimates using simulation
models:
• Case 1. One may have three different geological realizations (representing the low, best, and
high scenarios) and associated reservoir simulation models that can be used to directly
estimate the respective in-place volumes, EURs, Reserves (e.g., the EURs reduced for
cumulative production realized, if any), and/or Contingent Resources categories. This is
definitely preferred, but not a common practice given the time and expense to develop
several rigorous models.
• Case 2. One may only have a single integrated reservoir simulation model, which can be
used to directly estimate a single most likely (or best) value of project PIIP, EUR, Reserves,
and/or Contingent Resources. In deterministic analysis, it is common practice to run
sensitivity predictions to understand the range of uncertainty and assign the 1P and 3P
categories accordingly.
Irrespective of the assessment method used and amount and quality of necessary data
available, the estimates must fulfill the premise stated in Point 2 above before booking.
Expertise gained over many years of working with the reservoir simulation models and the
ability to select the model most appropriate for the oil and gas reservoir (or recovery project)
under evaluation are critically necessary skills required to complement a thorough understanding
and application of PRMS guidelines for the classification and categorization of petroleum
resources. However, it is absolutely critical to be realistic and pay attention to the following wise
and cautionary statement by Thiele (2010) that applies to all analyses, but specifically the
reservoir simulation: “The industry has long recognized the importance of quantifying
uncertainty. As a result, computational resources are being directed more toward simulating large
ensembles of models rather than adding ever increasing levels of detail and physics to a single
representation of the subsurface. For multimillion-dollar capital investments, it is far more
important to acknowledge the possibility of catastrophic outliers and invest in reducing
uncertainty by guided data acquisition than to tweak a single reality to excess.”
b.) Application to Example Oil Project. This application represents an oil recovery project at
its mature late-production and early-decline stages. The example oil reservoir was developed and
produced under a very effective down-dip water injection scheme over the past 16 years. During
that time, 36 new oil producers and 3 water injectors were also drilled (bringing the total to 70
and 22 wells, respectively) to better manage production decline and to help further improve
overall recovery efficiency.
Based on the extensive log, core, and testing data obtained over the past 19 years (discovery
year, 2-year appraisal period followed by a 3-year initial development and a 16-year of
production periods depicted by Fig.4.1a), a 0.5 million-cell geocellular model (similar to that
depicted in Fig. 4.6) was built and used to estimate an OIIP of about 1,525 MMSTB with a
reported single statement that “the results of sensitivity runs, using this geological model,
showed about a 6% downside (meaning 1,434 MMSTB) and a 14% upside (meaning 1,739
MMSTB) in the OIIP estimate.” It is important to note that since the Material Balance Analysis
Guidelines for Application of the Petroleum Resources Management System
60
of this example oil project was conducted 8 years ago, the range in the OIIP estimates were
reduced to a ratio of 1.21 (=1,739/1,434) from 1.54 (=2,000/1,300), a 21% reduction in the range
of both the OIIPs and the EURs (because of using the same recovery factors). Hence, the relative
degree of uncertainty in these estimates should also have been reduced.
Based on this most likely or best 3D geological realization (with an OIIP estimate of 1,525
MMSTB), a related integrated 3D and three-phase reservoir simulation model was developed by
a multidisciplinary team and used to match this extensive reservoir performance history covering
a period of 16 years with 399 MMSTB (26.2% OIIP) produced.
This history-matched black-oil model was used to predict future reservoir performance under
the ongoing base-case operations using peripheral waterflood, including economically justified
well workovers, infill drilling, and well completions to better manage the decline. The historical
and predicted profile for the Best Scenario (Base Waterflood) is presented in Fig. 4.9. As shown
in Fig. 4.9, an EUR of 686.3 MMSTB (45% OIIP) at an economic limit of about 2,700 BOPD
was predicted. It represented the most likely or the “best” scenario for the ongoing waterflood
performance already confirmed by the excellent performance observed over the past 16 years. It
confirmed the 45% OIIP recovery factor assigned 8 years earlier in Material Balance Analysis.
Example Oil Project: Production Performance Predictions and the Best EURs
Recovery Project
CO2 Miscible Flood
PWF with Artificial Lift (ESPs)
Peripheral Waterflood (PWF)
Oil Production Rate (Qt), MBOPD
100
Predicted
Qe(BOPD)
3,500
3,000
2,700
3
2
1
EUR (MMSTB)
1067.6 (C)
793.0 (B)
686.3 (A)
The "Best Estimate" of Model OOIP = 1,525 MMSTB
Historical Production
1,400
RE(% OIIP)
70
52
45
1,200
C
1,000
3
75
B
2
800
A
1
600
50
400
25
200
Economic Limit, Qe ~ 3 MBOPD
0
0
0
10
20
30
40
50
60
70
80
90
Time (t), years
Fig. 4.9—Dynamic and direct assessment of reserves using reservoir simulation.
100
Cumulative Production, MMSTB
125
Assessment of Petroleum Resources Using Deterministic Procedures
61
Based on the reported low and high estimates of OIIP from the sensitivity analysis and using
the respective same REs, the project EURs and Reserves were calculated. The results are
summarized in Table 4.8.
Table 4.8—Assessment using Reservoir Simulation Studies (Early-Decline Stage):
Estimates of Project PIIPs, EURs and Reserves Under Peripheral Waterflood Only
Bases and Estimates by Reserves Category
Measured and Estimated Parameters
Cumulative Production
- Oil
- Raw Gas
Original Oil In-Place (OIIP)
Recovery Factor1
Recoverable Oil (EUR)2
- Original
- Remaining*
Economic Oil Rate Limit
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Original Gas In-Place (GIIP)
Recoverable Raw Gas (EUR)1 - Original
- Remaining*
Units
Low Estimate
Best Estimate
High Estimate
MMSTB
% OIIP
Bscf
MMSTB
% OIIP
MMSTB
MMSTB
399
27.8%
227.4
1,434
40%
573.4
174.4
399
26.2%
227.4
1,525
45%
686.3
287.3
399
23.0%
227.4
1,739
50%
869.3
470.3
STB/D
scf/STB
Btu/scf
Bscf
Bscf
2,700
570
1,350
817.1
326.8
76.1
99.4
23.1
3,000
570
1,350
869.3
391.2
91.1
163.8
38.1
3,500
570
1,350
990.9
495.5
115.3
268.0
62.4
MMBOE3
Bscf
MMBOE 3
1
Waterflood RFs, calculated or implied for the Best Estimate and assigned for the Low and High Estmates.
The Best Estimate is obtained from the projected production profile of a project-specific Reservoir Simulation Study.
3
Calculated using an average conversion factor of 5.8 MMBtu per BOE
* Estimated Oil and Raw Gas Reserves categories of 1P, 2P and 3P, respectively.
2
The same black oil model was used to study a “what-if” reservoir performance scenario of
installing a fieldwide artificial lift facility using electrical submersible pumps (ESPs) in all oil
producers by the Year 21. Based on a conservative economic limit of about 3,000 BOPD, an
EUR of 793 MMSTB (or about 52% OIIP) was predicted for the combined project of peripheral
waterflood with artificial lift using ESPs. The project’s production profile and resulting estimates
are also presented in Fig. 4.9 to illustrate its performance relative to the ongoing peripheral
waterflood project without the ESPs. This 7% OIIP incremental “what-if” predicted performance
had confirmed the results of an earlier study and was supported by several nearby analog
artificial lift projects showing incremental economic recoveries as high as 9% OIIP.
The company committed to the ESP implementation. The additional recovery was judged to
have reasonable certainty and placed in the Proved category with Undeveloped status at the time,
expecting it to be transferred to Proved Developed in 4 years time (or in Year 21), when the
project was expected to be completed and put on-stream.
Although some may consider the artificial lift as a separate project, it was in fact a combined
project that just enhances the ongoing waterflood by only installing ESPs in some producers. In
actual practice, artificial lift is generally implemented in stages (especially with ESPs) to
minimize operating expenses because oil producers reach their critical water-cut levels at
different times making a fieldwide simultaneous installation as a separate project not as
attractive.
Guidelines for Application of the Petroleum Resources Management System
62
Irrespective of how one treats the artificial lift projects, their impact was incorporated with
the ongoing peripheral waterflood project by adding this constant 7% OOIP increase in recovery
efficiency to the recovery efficiency of each low, best, and high scenario estimated and/or
assigned in Table 4.8. The project increased the respective recovery efficiencies to 47%, 52%,
and 57% of the OIIPs. As a result, the respective EURs and reserves categories for the
“combined project” were recalculated and the results are now summarized in Table 4.8a.
Table 4.8a—Assessment using Reservoir Simulation Studies (Early-Decline Stage):
Estimates of Project PIIPs, EURs and Reserves under Peripheral Waterflood with ESPs
Bases and Estimates by Reserves Category
Measured and Estimated Parameters
Cumulative Production
- Oil
- Raw Gas
Original Oil In-Place (OIIP)
Recovery Factor1
Recoverable Oil (EUR)2
- Original
- Remaining*
Economic Oil Rate Limit
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Original Gas In-Place (GIIP)
Recoverable Raw Gas (EUR)1 - Original
- Remaining*
Units
Low Estimate
Best Estimate
High Estimate
MMSTB
% OIIP
Bscf
MMSTB
% OIIP
MMSTB
MMSTB
399
27.8%
227.4
1,434
47%
673.7
274.7
399
26.2%
227.4
1,525
52%
793.1
394.1
399
23.0%
227.4
1,739
57%
990.9
591.9
STB/D
scf/STB
Btu/scf
Bscf
Bscf
2,700
570
1,350
817.1
384.0
89.4
156.6
36.5
3,000
570
1,350
869.3
452.0
105.2
224.6
52.3
3,500
570
1,350
990.9
564.8
131.5
337.4
78.5
MMBOE3
Bscf
MMBOE3
1
Waterflood RFs, calculated or implied for the Best Estimate and assigned for the Low and High Estmates.
The Best Estimate is obtained from the projected production profile of a project-specific Reservoir Simulation Study.
3
Calculated using an average conversion factor of 5.8 MMBtu per BOE
* Estimated Oil and Raw Gas Reserves categories of 1P, 2P and 3P, respectively.
2
Furthermore, based on the same geological model representing the best case scenario, and
relevant CO2 and hydrocarbon compositional data (including miscibility test results), an
integrated compositional model was developed to study the performance of CO2 miscible
displacement process and several alternatives using different water-alternating-gas (WAG)
scenarios. Assumed to be on stream by Year 21 (similar to the “what-if” artificial lift study),
several production performance predictions were carried out to a 3,500 BOPD economic limit,
yielding a cumulative oil recovery of about 1,068 MMSTB (or 70% OIIP) for the case with an
optimum CO2 injection at the crest. The results for this best-case scenario are also presented in
Fig. 4.9 to illustrate its performance relative to the ongoing base peripheral waterflood and the
second peripheral waterflood with artificial lift projects. This predicted incremental recovery of
18% OIIP for a CO2 EOR project was supported by two CO2 pilots already implemented in
analog oil projects nearby and yielding a reported maximum recovery efficiencies of 22% OIIP,
which established the upper limit.
Although the project economics were positive, it was not reasonably certain that the project
would be implemented in Year 21 as initially assumed. The infrastructure for sequestration and
delivery of CO2 to the project site were assessed to take longer and delayed because of the
expected budgetary constraints at the time. Consequently, the estimated recoverable quantities of
Assessment of Petroleum Resources Using Deterministic Procedures
63
oil and raw natural gas were classified as Contingent Resources. Therefore, incremental
recoverable quantities attributable to CO2 miscible project had to be separated from the second
project and reported incrementally (using a low and a high recovery efficiency of 15% and 22%
OIIP, respectively, to bracket uncertainty) as shown in Table 4.8b. There was a note stating that
“these estimates should be reviewed periodically to confirm whether these unfulfilled
contingencies still exist and if fulfilled, they can be classified as Reserves.”
Table 4.8b—Assessment of Contingent Resources using Reservoir Simulation Studies
(Early Decline Stage): EURs Under a Planned CO2 Miscible Flood Project
Bases and Estimates by Resource Category
Measured and Estimated Parameters
Original Oil In-Place (OIIP)
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Recovery Factor1
Recoverable Oil*
Recoverable Raw Gas*
Units
Low Estimate
Best Estimate
High Estimate
MMSTB
scf/STB
Btu/scf
% OIIP
MMSTB
Bscf
MMBOE3
1,434
570
1,350
15%
215.0
122.6
28.5
1,525
570
1,350
18%
274.5
156.5
36.4
1,739
570
1,350
22%
382.5
218.0
50.7
2
Under a CO2 Miscible Flood based on the results of two implemented anolog CO2 Pilot Projects.
3
Calculated using an average conversion factor of 5.8 MMBtu per STB of crude oil.
Estimated Oil and Gas Contingent Resources categories of 1C, 2C and 3C, respectively.
*
Production Performance Trend (PPT) Analyses. PPT analyses have proved to be very
useful and commonly used methods to directly estimate the EURs for oil and gas wells,
reservoirs and specific development (or recovery) projects. Although PPT analyses are
traditionally known as decline curve analyses (DCAs), other forms of PPTs exist and can also be
used to estimate petroleum (oil and gas) reserves. Historical production performance trends
observed in mature wells, reservoirs, or projects may generally be extrapolated to the cumulative
production at the economic limit, and provide a reasonable assessment of the EUR. Moreover,
the predicted production rate profiles obtained using analytical or reservoir simulation studies
could establish performance trends that are not long enough to include the project’s economic
life. In these cases, the DCA can also be used to best-fit these trends and extrapolate them all the
way to project economic limit and determine the EURs.
To better comprehend the limitations of PPT analysis, Harrell et al. (2004) pointed out the
following conditions under which production decline trends would provide acceptable
projections of production profiles and the resulting reserves estimates for the asset under study:
• Production conditions, methods, and the overall production strategy are not changed
significantly over the projected remaining producing life.
• The reservoir has been fully developed, and therefore, the well count is relatively stable.
• Wellbore interventions and other remedial work can be classified solely as maintenance.
Production performance trends are not only reservoir specific but also depend on the specific
reservoir management and production practices used. Any significant change in these practices
could easily lead to erroneous results. Therefore, the reliability of production profiles projected
using DCA depends not only on the quality and quantity of the past production data, but also on
the evaluator’s professional experience gained through working on many hands-on assessments
and reconfirmations of results over time with actual performance, including the use of
appropriate analog reservoirs.
Guidelines for Application of the Petroleum Resources Management System
64
a) Technical Principles. Decline analysis is based on the solution of the following differential
generalized hyperbolic equation defining the nominal decline rate (D) as the fraction of “change
in production rate with time (t)” (also known as loss ratio) as
Dt = -(dQ/dt)Qt = KQb ,
(4.8)
where
Dt = nominal (or continuous) decline rate (slope of the line) at any time (t) and is a fraction of
production rate (Qt) with a unit of reciprocal time (1/t) in per month, per year, etc, which
must be consistent with the units of production rate,
Qt= production rate (STB/D, STB/month or STB/yr),
b = decline exponent, and
K= integration constant
Decline trends analysis of production rate vs. time advanced by Arps (1945) is a hyperbolic
equation similar to Eq. 4.8, and therefore, it has a semitheoretical basis. The PPTs and their
extrapolations to the economic limit are governed by the mathematical equations (as solutions to
hyperbolic differential Eq. 4.8) summarized in Table 4.9 below.
Table 4.9—Traditional Decline Analysis:
Governing Equations and Characteristic Linear Plots
dQ
Generalized governing hyperbolic decline equation:
Hyperbolic Model
Nominal Decline Rate (D)
(Dt /Di) = (Qt/Qi)b
Decline Exponent (b)
“b” varies
except for 0 & 1
Rate -Time
Relationships
Harmonic Model
Dt =Di = Dt=constant
(Dt /Di) = (Qt/Qi)
b=0
b=1
Qt = Qi e-Dt
logQt = logQi – (1/b)log(1+bDi t)
logQt = logQi - (D/2.3) t
log Qt vs. log (1+C t)
log Qt vs. t
Not available
1/Qt vs. t or
log Qt vs. log (1+Di t)
]
Npt = (Qi-Qt)/D
Qt vs. Npt
N pt
=
︶
Qt=Qi-D Npt
Type of Linear Plots
logQt = logQi - log(1+Di t)
i
︵
Qt =.Qi (1+Di t)-1
b
1
︶
Qt
-
[︵
b
1
︶
Di
︵
Qi
t
p
b
Qi1
=
N
b
where C=bDi
Rate-Cumulative
Relationships
dt = KQ b
Q
Exponential Model
Qt =Qi [1+nDi t] (-1/b)
Type of Linear Plots:
D=−
Q
ln ( Qi / Q )
Di
logQt=logQi–Di /(2.3 Qi) Npt
log Qt vs. Npt
i = stands for initial time or point at which the decline trend has onset or started.
Dt = nominal decline rate (as fraction of Qt) with a unit of inverse time (1/t), equals to Di when Qt= Qi.
Qt = oil or gas production rate at any time “t” in STB/D or MMscf/D, etc*.
t = time and the subscript for oil rate & cumulative production variables*.
Npt = cumulative oil or gas production or oil recovery at any time “t” in consistent units*.
* Rate (Q) & time (t) must be in consistent units in above formulae (i.e., if “Q” is in STB/D, “t” is in days, etc.).
Well-known and widely used DCAs provide a visual illustration of historical production
performance of a well, a group of wells, or a reservoir and of whether the established trend can
be extrapolated to the economic limit to estimate petroleum reserves. Review, derivation, and
understanding of these governing equations and the characteristic linear plots (summarized in
Table 4.9) representing each decline model are very important for correct use and application of
the traditional DCA. Note that the exponential and harmonic models are just specific cases of the
hyperbolic model with constant decline exponent (b) of 0 and 1, respectively.
Assessment of Petroleum Resources Using Deterministic Procedures
65
The hyperbolic decline model is not only the most common decline trend observed in the
actual performance of oil and gas wells and reservoirs, but also represents the most general and
challenging decline trend with two unknown parameters of initial nominal annual decline rate
(Di) and decline exponent (b). Moreover, the hyperbolic decline exponent (b) is not fixed but
varies, and may have any value except b = 0 and b = 1, which represent the special cases defined
by exponential and harmonic models, respectively, among wells and reservoirs producing under
different reservoir depletion methods. It has been widely reported that the value of (b) varies
with reservoir drive mechanism. Although the development of unconventional reservoirs in
North America has resulted in observed “b” values significantly exceeding one, the following
values generally applicable to conventional reservoirs reported by Fekete Associates (2008) may
be used:
Value of Decline Exponent (b)
0
0
0.1-0.4
0.4-0.5
0.5
0.5-1.0
Governing Reservoir Drive Mechanism
Single-phase liquid (oil above bubblepoint)
Single-phase gas at high pressure
Solution gas drive
Single-phase gas
Effective edgewater drive
Comingled layered reservoirs
Initial nominal decline rate (Di) is the nominal (or continuous) decline rate corresponding to
initial production rate at which decline begins. The ratio of nominal decline rate at any time (t)
(or Dt) to initial decline rate (Di) when production decline first begins is proportional to a power
(b) (except 0 and 1) of the respective production rates and defined by
Dt /Di = (Qt /Qi)b
(4.9)
Rate of decline depends on several factors, such as the reservoir depletion rate, maturity, the
average reservoir pressure, the reservoir rock and fluid properties (magnitude and distribution),
and the reservoir management and production practices. The Di is further related to the initial
effective (or stepwise) decline rate (di), which is a step function rather than a continuous function
between two consecutive rates, by the following relationship:
di= 1 – [1+ b Di]-(1/b) .
(4.10)
For example, if Di =0.25/yr and b =0.5, then di = 1-[1- 0.5 x 0.25]-(1/0.5) = 0.21/yr.
The governing rate-time relationship of a general hyperbolic decline model (see Table 4.9) is
given by
Qt = Qi (1+ b Di t)-1/b .
(4.11a)
Eq. 4.11a may also be re-written as:
log Qt = log Qi - (1/b) log (1+ b Di t) = log Qi - (1/b) log (1+ C t),
(4.11b)
where C = b Di, which is an arbitrarily defined unknown constant (refer also to Table 4.9).
For a correct value of C, Eq. 4.11b suggests that a log-log plot of Qt vs. (1+ C t) should yield
a straight line with a slope, m (= -1/b) and intercept of Qi at time zero or when initial decline
starts.
Given the initial production rate at the onset of decline (Qi) and other oil production data
observed over the decline period, the traditional and modern DCAs have been largely an exercise
in curve fitting to establish characteristic straight lines and/or type curves and conducting
nonlinear regression analysis to simultaneously estimate the correct values of these two unknown
Guidelines for Application of the Petroleum Resources Management System
66
︶
b
1
e
[︵
Q
-
Q
b
1
i
︶
Di
EUR = N pe = N pi + N pde = N pi︵
+
b
Qi1
b
parameters Di and b. Hyperbolic decline is known to occur as gravity drainage becomes the
dominating reservoir drive mechanism during later stages of a well life. However, it is possible
for this trend to become exponential again at a later stage when the solution GOR is very low and
stabilized. With estimated correct values of these two unknowns, the EUR defined by the
cumulative production at economic limit, Npe, of the petroleum asset under evaluation can now
be directly calculated using the following relationship:
︵
︶
]
(4.12)
where Qi and Qe represent the production rate at initial time (i) or time zero (t=0) or at the onset
of decline and at economic limit (e), respectively; and Npi, Npde and Npe represent the cumulative
production all the way to the initial (i) production rate (Qi) or to time zero (t=0) before decline
begins, during the entire decline period (de) analyzed all the way to economic limit, and overall
project to economic limit (e) or the EUR, respectively.
b.) Types of PPT Analysis. Various well-established methods using PPT analyses may be
classified and described under three broad categories: traditional DCAs (TDA), modern DCAs
(MDA), and other PPT analyses.
Traditional Decline Analysis (TDA). A trial-and-error procedure is used to calculate sets of
values for (1+ C t) for several assumed values of C and generating the resulting “log Qt vs. log
(1+ C t)” plots until a straight line is obtained. As shown in Eq. 4.11b, for a correct value of C
(an arbitrary constant defined by the multiplication of these two unknown decline parameters n
and Di), the slope (m = -1/b) of such a log-log plot should yield the value of decline exponent (b
= -1/m) and the initial decline rate is estimated using Di = C/b. However, the practical use of this
method is limited. It is extremely difficult to quantitatively evaluate the correct value of the
decline exponent (b) because it is very insensitive to this type of analysis attempting to estimate
two unknowns (C and b) simultaneously and usually yielding erroneous results. It is quite
possible to have the same “b” but different Di’s matching the same decline trend that extrapolates
to different estimates of reserves. Hence, this procedure is not recommended.
It would be highly desirable to estimate the nominal decline (Di) first and then perform a
simple trial-and-error procedure iterating on this single insensitive decline exponent (b) to
evaluate and establish the best-matched decline trend that corresponds to the best value of (b). In
this regard, a method similar to that recommended by Exxon Production Research Company
(EPRCO 1982) proved to be very useful in actual practice. It uses the following a 7-step
procedure described and applied to the analyses of this example oil project below.
Application of TDA to Example Oil Project. The project produced under peripheral water
injection over the past 26 years with a cumulative production of 518.9 MMSTB. Production
decline started at the beginning of Year 11. During the latest 10-year period (Years 17 through
26), an additional production of 120 MMSTB was realized by drilling an additional 12 new oil
producers and 3 water injectors, bringing the total to 82 oil producers and 25 water injectors.
Note that caution is warranted anytime DCA is used at a level of aggregation beyond the well or
completion. Changing well count with time and operational adjustments can alter the shape of
the aggregated curve in an unpredictable manner. Please refer to section 6.2.1 for further
discussion.
Historical decline observed over the past 16 years, with quarterly average production rates
reported during the last 5 years to better illustrate possible variations, were used to draw and
establish three slightly different plausible decline trends and to estimate the associated annual
nominal decline rates (Di’s) that reflect the uncertainty in the observed production data and
Assessment of Petroleum Resources Using Deterministic Procedures
67
interpolations. With a total of 82 wells already producing (and only 10 infill wells remaining),
the well count is judged to be reasonably stable enough not to significantly impact these decline
rates. The resulting Di’s and the observed decline data were used to estimate the related
hyperbolic decline exponents (b’s) from the respective best-fit trends obtained. These three
plausible decline trends or interpretations are judged to quantify the degree of uncertainty in the
estimates of respective decline parameters and the extrapolations of these established trends to
estimate the reserves (or cumulative production) for low, best, and high scenarios at their
respective project economic limits.
The following EPRCO procedure is used to establish the plausible annual decline rates (Di’s)
and associated decline exponents (b’s) that yield the best fit for three possible hyperbolic decline
trends established for the example oil project:
Step 1. Prepare a “Qt vs. time (t)” (instead of “log Qt vs. t”) plot and draw the best smooth curve
through data (quarterly average production rate data are used for the last 5 years to help better
show the variations), but giving the greatest weight to and matching the latest data as closely as
possible as illustrated in Fig. 4.10a. Note that the EPRCO recommended semi-log plot of “log Qt
vs. time (t)” almost eliminates the variations in the observed production data and hence does not
allow for more than one interpretation and thus it was not used.
Example Oil Project: A Trial-and-Error Procedure for Establishing correct Di and n
Three Possible Interpretations using "Oil Rate vs. Time" plot
100
Results of Matching Process Yielding the Best "b" Value
Production Rates using Eq. 4.11a
Decline Parameters Used
Scenario
Calculated
Avg. Deviation D i (Annual) Best "b" Value
90
Oil Rate (Qt), MBOPD
80
Low
Best
High
70
60
1.8%
1.6%
1.9%
5.8%
5.3%
4.8%
0.40
0.55
0.75
Historical Oil Rates
Smooth curve through the declining rates
50
40
30
Q = 25
Estimation of Decline Rates*
Tangent @ Q i = 25 MBOD
Slope, m = D i (Annual)
5.8%
5.3%
4.8%
* Three possibilities bracketing the uncertainty
20
10
0
10
12
14
16
18
20
22
24
26
28
30
Time (t), years
Fig. 4.10a—Estimation of hyperbolic decline parameters using rate vs. time plots.
Step 2. Draw a series of three plausible straight lines as tangents to the curve at a point near the
latest values of production rate at a time (t = 26 years) and production rate (Qt = 25 MBOPD) to
estimate the respective slopes (m) and hence the nominal decline rates (Dt). Fig. 4.10a illustrates
how this process works and summarizes the resulting hyperbolic decline parameters.
Step 3. Assume several plausible values of (b) and use any value of Di (5.3% per year for the
best scenario for instance) and Eq. 4.11a to calculate the production rates for various negative
values of time (t). Time is negative because decline rate is determined at the most recent time (t =
Guidelines for Application of the Petroleum Resources Management System
68
25.75 years) when Q = 25 MBOPD and times between this rate and earlier Q’s all the way to Qi
of 75 MBOPD (initial rate at which production decline began) must have negative values to
satisfy Eq. 4.11a.
Step 4. Plot both the calculated values of Q’s for various plausible values of b obtained in Step 3
and the actual production rate data to establish the best b value for the best-fit curve that has the
least average deviation. Calculated data with an b value of 0.55 (shown with hollow circles in
Fig. 4.10a) yielded the best-fit to actual data (shown in black dots) with a minimum average
deviation of about 1.6%.
Step 5. Repeat Steps 3 and 4 with the remaining annual decline rates of 5.8% and 4.8%, to
determine the best “b” values of 0.40 and 0.75 for the low and high scenarios, yielding best-fits
with minimum average deviations of about 1.8% and 1.9%, respectively. Fig. 4.10a presents the
results obtained using the above five-step process.
Step 6. Use the correct decline exponent (b) of 0.55 and the nominal annual decline rate 5.3% at
25 MBOPD and Eq. 4.9 to calculate the initial nominal annual decline rate (Di) of 9.7% at Qi of
75 MBOPD (initial rate at which production decline began) for the best-case scenario. Similarly,
values of Di of 9.0% and 11.1% are calculated for the low and high case scenarios, respectively.
Step 7. Finally, for the best scenario for example project operating under peripheral down-dip
water injection scheme, use Di = 9.7% and b = 0.55 and Eq. 4.11a to calculate the oil production
rate profile and the cumulative production from Qi of 75 MBOPD (end of Year 10 when decline
first begins) to economic limit determined to be about 3.2 MBOPD and determine the portion of
cumulative production over the whole decline period (Npde). Then use Eq. 4.12 to calculate the
total EUR (or Npe) of 747.3 MMSTB and the 2P Reserves of 228.4 MMSTB (the EUR adjusted
for the cumulative production of 518.9 MMSTB), which illustrated and reported in Fig. 4.10b.
Note that for the best-case scenario, the same results can be obtained by using Di of 5.3% and b
of 0.55 to forecast oil rates and cumulative production from Qi of 25 MBOPD (end of Year 26)
to the same economic limit and adding to it the cumulative production realized during the first 26
years, etc.
Example Oil Project: Production Performance Trend Analysis
"log (Qt) vs. log (t)" Characteristic Plot for the Hyperbolic Decline Model
100
The Best Estimate of Peripheral Water Injection Performance:
Using the matched decline parameters from Fig. 4-10a for the
Best Estimate, calculated the Di = 9.7% @ Qi=75 MBOPD
(when decline stats) using Eq. 4.9 .
Oil Rate (Qt), MBOPD
OIIP = 1,525 MMSTB
Np = 518.9 MMSTB (34% OIIP)
EUR = 747.3 MMSTB (49% OOIP)
2P Reserves = 228.4 MMSTB
(15% OIIP or 30.6% EUR)
10
Historical Production Performance
Matched Hyperbolic Decline Trend using Eq. 4.11a
with Di = 9.7% @ 75 MBOPD and b = 0.55
Qe, Economic Limit of 3,300 BOPD
1
1
Dimensionless Time, tD = (1 + bDit)
Fig. 4.10b—Dynamic and direct assessment of reserves by TDA.
10
Assessment of Petroleum Resources Using Deterministic Procedures
69
Fig. 4.10b is a resulting characteristic linear plot of “log Qt vs. log (1+ b Di t)” for the bestestimate scenario only. High-quality matches obtained from using the EPRCO procedure is
clearly demonstrated by the actual data (represented by black dots) relative to the calculated data
(represented by hollow diamonds, circles, and squares) in Fig. 4.10a and the resulting similar
high-quality decline trend match obtained in the characteristic linear plot of Fig. 4.10b for the
best scenario only (for simplicity in the presentation). It confirms a higher-quality match
obtained using a more reliable and repeatable EPRCO procedure of estimating these unknown
decline parameters sequentially. The traditional trial-and-error method attempts to estimate the
complex arbitrary constant C (= b x Di) and b simultaneously, usually yielding erroneous results
because the evaluation of “b” is known to be very insensitive to this procedure [Fekete
Associates (2008)].
Table 4.10 documents the resulting reserves categories of 1P, 2P, and 3P estimated based on
the plausible scenarios of low, best, and high production performance analyzed and exhibited
above, which was supported by the example project under an effective peripheral water injection
operation (without artificial lift using ESPs) observed over the past 26 years.
Based on the similar reasons and rationale developed and discussed earlier under Reservoir
Simulation Methods, the second combined peripheral waterflood with artificial lift project was
expected to have additional oil recovery of 5% OOIP over peripheral waterflood, bringing the
project recovery to 54% OOIP for the best scenario. Similarly, these additional reserves are
placed in Proved with Proved Undeveloped status for now, subject to transfer to Proved
Developed in 2 years (or in Year 28) when the project is expected to be completed and put onstream.
Table 4.10—Assessment using Decline Curve Analysis (Production Decline Period):
Estimates of Project EURs and Reserves under Peripheral Waterflood only
Bases and Estimates by Reserves Category
Measured and Estimated Parameters
Cumulative Production
- Oil
- Raw Gas
Original Oil In-Place (OIIP)
Recovery Factors Calculated1
- Original
Recoverable Oil (EUR)
- Remaining*
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Original Gas In-Place (GIIP)
Recoverable Raw Gas (EUR) - Original
- Remaining*
1
Units
Low Estimate
Best Estimate
High Estimate
MMSTB
% OIIP
Bscf
MMSTB
% OIIP
MMSTB
MMSTB
scf/STB
Btu/scf
Bscf
Bscf
MMBOE2
Bscf
MMBOE2
518.9
34.0%
295.8
1,525
46.0%
701.5
182.6
570
1,350
869.3
399.9
93.1
104.1
24.2
518.9
34.0%
295.8
1,525
49.0%
747.3
228.4
570
1,350
869.3
426.0
99.1
130.2
30.3
518.9
34.0%
295.8
1,525
54.0%
823.5
304.6
570
1,350
869.3
469.4
109.3
173.6
40.4
As a ratio of "direct estimates of project EURs under peripheral water injection" and "the project OIIPs, if available".
Calculated using an average conversion factor of 5.8 MMBtu per BOE
* Estimated Oil and Raw Gas Reserves categories of 1P, 2P and 3P, respectively.
2
Guidelines for Application of the Petroleum Resources Management System
70
Table 4.10a summarizes the resulting EURs and reserves categories for the peripheral
waterflood with artificial lift project as they were recalculated using the increased recovery
efficiencies of 51%, 54%, and 59% of the OIIPs. The estimates were considered to have a
slightly reduced degree of uncertainty relative to those obtained under the peripheral waterflood
project only (refer to Table 4.10).
It may be important to point out the following qualifications about the oil example project
producing under peripheral water injection:
1. As summarized in Table 4.10, the low, best, and high project EURs and Reserves are
estimated directly. Although it was not necessary to know the latest estimates of respective
OIIPs, it would have been definitely desirable. They were not available at the time. For a
relative illustrative purpose, the best estimate of 1,525 MMSTB was used to show the
recoverable estimates in terms of percent OIIP as well, and to report in respective figures and
tables.
2. Since last assessment using reservoir simulation models, the project had produced another
120 MMSTB, bringing the total to 518.9 MMSTB (34% of OIIP) in 26 years, drilled and
analyzed 15 additional new wells, and obtained numerous well tests thereby reducing
uncertainty in the new estimates. The EURs represented relative waterflood recovery
efficiencies of 46%, 49%, and 54% of this single OIIP estimate, respectively and correspond
to project economic lives (at around 3 MBOD) estimated to be 78, 96 and 127 years,
respectively. Long economic and/or operation project lives such as these should not be
assumed without proper consideration and documentation. In this example the estimates
were considered valid because:
• The best or 2P estimate of 228.4 MMSTB (or remaining reserves) represents only 15%
OOIP or about 30% of the 747.3 MMSTB EUR (see Table 4.10).
• In actual practice, for projects with long-life reserves exceeding 100 years, depending on
the sustainable future growth in worldwide demand for oil, the project’s economic life
will most likely vary between 50 and 70 years as a natural consequence of the higher
depletion rates, which are not only required to meet the expected target production rates,
but also result from implementation of the approaching planned artificial lift using ESPs
and EOR projects. They are needed to both accelerate production (e.g., higher depletion
rates) and increase ultimate recovery.
The incremental 7% OOIP oil recovery by artificial lift using ESPs (discussed earlier under
Reservoir Simulation Methods) was revised downward to 5% OOIP to further ensure reasonable
confidence and to bring the overall project recovery to 54% OOIP for the “best scenario.”
Similarly, these additional reserves are placed in Proved Undeveloped status for now, subject to
Proved Developed status in 2 years (or in Year 28), when the project is expected to be completed
and put on-stream.
Table 4.10a summarizes the resulting EUR’s and reserves categories for the peripheral
waterflood with artificial lift project recalculated using the increased recovery efficiencies of the
peripheral waterflood by a constant 5% OIIP to 51%, 54%, and 59% of the OIIPs.
Assessment of Petroleum Resources Using Deterministic Procedures
71
Table 4.10a—Assessment using Decline Curve Analysis (Production Decline Period):
Estimates of Project EURs and Reserves Under Peripheral Waterflood with ESPs
Bases and Estimates by Reserves Category
Units
Measured and Estimated Parameters
Cumulative Production
- Oil
- Raw Gas
Original Oil In-Place (OIIP)
Assigned Recovery Factors 1
Recoverable Oil (EUR)
- Original
- Remaining*
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Original Gas In-Place (GIIP)
Recoverable Raw Gas (EUR) - Original
- Remaining*
Low Estimate
Best Estimate
High Estimate
518.9
34.0%
295.8
1,525
51.0%
777.8
258.9
570
1,350
869.3
443.3
103.2
147.5
34.3
518.9
34.0%
295.8
1,525
54.0%
823.6
304.7
570
1,350
869.3
469.4
109.3
173.7
40.4
518.9
34.0%
295.8
1,525
59.0%
899.8
380.9
570
1,350
869.3
512.9
119.4
217.1
50.5
MMSTB
% OIIP
Bscf
MMSTB
% OIIP
MMSTB
MMSTB
scf/STB
Btu/scf
Bscf
Bscf
MMBOE 2
Bscf
MMBOE 2
1
Under peripheral water injection (see Table 4.10), supplemented with field-wide installed artificial lift using ESPs.
2
Calculated using an average conversion factor of 5.8 MMBtu per BOE
* Estimated Oil and Raw Gas Reserves categories of 1P, 2P and 3P, respectively.
Similarly, supported further by the full performance of a second analog CO2 miscible pilot
project nearby with a realized recovery efficiency of about 20% OIIP, it was judged prudent to
revise the incremental recovery efficiencies (assigned earlier under Reservoir Simulation
Methods) downward by 2% to 13%, 16%, and 20% OIIP, respectively, bracketing the
uncertainty for the planned CO2 miscible project (scheduled to be on-stream by Year 32). The
respective Contingent Resources categories of 1C, 2C, and 3C are summarized in Table 4.10b.
Table 4.10b—Assessment of Contingent Resources (Production Decline Period):
EURs under a Planned CO2 Miscible Project
Bases and Estimates by Resource Category
Measured and Estimated Parameters
Units
Low Estimate
Best Estimate
High Estimate
Original Oil In-Place (OIIP)
Initial Solution Gas-Oil Ratio (Rsi)
Gross-Heating Value of Raw Solution Gas
Recovery Factor1
Recoverable Oil*
Recoverable Raw Gas*
MMSTB
scf/STB
Btu/scf
% OIIP
MMSTB
Bscf
MMBOE3
1,525
570
1,350
13%
198.3
113.0
26.3
1,525
570
1,350
16%
244.0
139.1
32.4
1,525
570
1,350
20%
305.0
173.9
40.5
2
Under a CO2 Miscible Flood based on the results of two implemented anolog CO2 Pilot Projects.
3
Calculated using an average conversion factor of 5.8 MMBtu per STB of crude oil.
Estimated Oil and Gas Contingent Resources categories of 1C, 2C and 3C, respectively.
*
Modern Decline Analysis (MDA). Similar to TDA, the objective of MDA is also to determine
the best-fit values of constants n and Di to the observed production rate trend for a well, a
number of wells or the entire reservoir. While not illustrated in this particular example oil
recovery project, advances in computing have facilitated the application of MDA using typecurve analysis and nonlinear regression techniques. Among many available in the literature,
Guidelines for Application of the Petroleum Resources Management System
72
these following two methods are judged to be significantly different and may be used to analyze
PPTs using MDA:
1. Fetkovich Type-Curve Analysis (Fetkovich 1980 and Fetkovich et al. 1987).
2. Hsieh et al. Dual Exponent Power Function Model (Hsieh et al. 2001). PS-CIM (2004)
provides a procedure for using spreadsheet software analysis with automatic curve-fitting
options to use and apply the Hsieh et al. (2001) method.
Examples for and discussion of these and other methods of both TDA and MDA can also be
found in various published articles by Long and Davis (1988), Mannon and Porter (1989), and
COGEH Volume 2 (2005).
1. Other Production Performance Trend Analyses. There are other well-established production
performance analyses that may be used to predict recoverable volumes based on trends
exhibited for a well and/or a reservoir even before the production rate begins to decline.
These reservoir drive specific analyses are briefly discussed by Cronquist (2001). Salient
points of these methods may be summarized as follows: 2. Cumulative Gas Production vs. Oil Production Trends: For oil reservoirs with solution-gas
drive, a semi-log plot of log Gp vs. Npt may develop a trend that could be extrapolated to
estimate oil recovery with the maximum Gp being equal to original solution gas in-place
(GIIP = Rsi x OIIP).
3. Water Cut or Water/Oil Ratio (WOR) vs. Cumulative Production Trends: These performance
trends have been found particularly useful in analyzing an oil reservoir with waterdrive or
producing with down-dip water injection and pattern waterflood. The established trend is
extrapolated to economic water cut (fw) or WOR to estimate ultimate recovery under the
prevailing production method over which the trend has been established. It may be useful to
note the following reported observations:
• A semi-log plot of “log fw (or fo) vs. Npt” trend may turn down at small values fo but
earlier for light oils and later for viscous oils (Brons 1963).
• A semi-log plot of (WOR+1) and total fluids withdrawal (Fp) vs. time (t) may help define
oil rate trend (Purvis 1985). It is reported that a semi-log plot of “(WOR+1) vs. Npt” tends
to be linear at WOR’s less than 1 and therefore may help define performance trends at
low values of WOR or water cuts.
• Ershaghi and Omoregie (1978) and Ershaghi and Abdassah (1984) recommended that a
plot of [1/fw-ln(1/fw-1)] vs. Npt should be linear. However, they noted that due to the
inflection point of the fw vs. Sw curves, the method will work only at higher water-cuts
when fw > 50%.
It logically follows that one should use Purvis-type performance trend analysis for reservoirs
with low water-cuts, and the Ershaghi et al.-type for those with high water-cuts exceeding 50%.
Finally, it must be emphasized that although the significant portion of semi-log plot of (krw/kro)
vs. Sw is linear, the floodout performance of wells and reservoirs are also governed by the rock
heterogeneity and the combined impact of gravity, viscous and capillary forces.
Actual PPT analyses require a thorough understanding of their semitheoretical technical
bases and the well-established and widely used methods and procedures. However, the correct
application of these procedures is not straightforward. One could easily and incorrectly obtain an
excellent match, but end up with inaccurate reserves. COGEH Volume 2 (2005) provides the
following advice on this very point: “The choice of the best-estimate case reserves, which
represents the 2P reserves estimate, must consider the quality of the fit, the uniqueness of the fit,
Assessment of Petroleum Resources Using Deterministic Procedures
73
the range of expected exponents, and the reasonableness of the reserves or life. Caution must be
used however in relying on computer generated best-fits, because there is always reservoir
uncertainty and late time behavior, which may change decline rates and exponents in the future.”
Production performance trends are not only reservoir specific but also depend on the specific
reservoir management and production practices. Any significant change in these practices could
easily shift and change the previously established decline trends and invalidate their
extrapolations. Therefore, proper application of these procedures, to a large extent, depends on
the experience and skill levels of the professional evaluators and their ability to judge the
reasonableness of results obtained by comparing them to known analogs and/or other
performance-based methods.
4.3 Summary of Results
Consistent with PRMS guidelines on petroleum resources and reserves definitions, classification,
and categorization, different deterministic assessment methods and procedures have been used to
estimate oil and raw gas resources and reserves for an example oil project. The project retraces
its E&P life cycle, starting from the exploration (pre- and post-discovery stages) and appraisal
phase and going through all three stages (including initial development) of its production phase
(refer to Figs. 4.1 and 4.1a). It covers 5-year appraisal and initial development period after the
initial discovery followed by an actual production history of 26 years.
Results of project’s OIIPs and EURs of oil resources and reserves estimated using
Volumetric and Analogous Methods during its Exploration and Appraisal Phase and Initial
Development Period are summarized in Fig. 4.11.
Estimates of OIIP, EURs and Reserves using Volumetric and Analogous Methods
Units: MMSTB (% OIIP)
OIIP
2,010
OIIP
1430
1,420
758
Prospective
Resources
65 (35%)
Pre-Discovery Stage
Source:
1,123
Contingent
Resources
3C
Table 4.1
1,271
Reserves
904 (45%)
771
639 (45%)
303 (40%)
186
OIIP
1,753
OIIP
406
2C
449 (40%)
1C
142 (35%)
3P
789 (45%)
2P
508 (40%)
1P
270 (35%)
Reserves
715 (50%)
643
Contingent
572
Resources
214 (15%)
Post-Discovery Stage
Appraisal Stage
Initial Development
Table 4.2
Table 4.3
Table 4.4
Fig. 4.11— Project Resources and Reserves Assessment during Exploration and Appraisal Phase.
Similarly, the results of estimated project OIIPs, EURs, and Reserves using performancebased methods at three different periods during its production phase are presented in Table 4.11.
Finally, based on these project OIIPs and the results of nearby analog pilot projects and
supported by a reservoir simulation study carried out for the example oil project, the estimated
respective Contingent Resources under a planned CO2 Miscible Project are summarized in Table
4.12. A close examination of Fig. 4.11, Tables 4.11 and 4.12 should provide a reasonable picture
Guidelines for Application of the Petroleum Resources Management System
74
of how estimates of project in-place and recoverable quantities (reserves and/or resources) could
change over its E&P life cycle.
Table 4.11—Reserves Assessment Using Performance-Based Methods
Estimates of Project OIIPs, EURs and Reserves During Production Phase
Estimates under Waterflood
Performance only
Assessment Method
Units
Low
Best
Estimate under Waterflood and
Artificial Lift Performance
High
Low
Best
High
Material Balance (MB) Analyses (Source: Table 4.6)
Early Production Stage with 8 years of actual production performance
Depletion Stage
Cumulative Production
MMSTB
220.8
220.8
220.8
(An indication of Project Maturity)
% OOIP
17.0%
13.8%
11.0%
MMSTB
Original Oil In-Place (OIIP)
1,300
1,600
2,000
Recovery Factor (%OIIP)
% OOIP
40%
45%
50%
MMSTB
Recoverable Oil (EUR)
520
720
1,000
MMSTB
Oil Reserves
299
499
779
Reservoir Simulation Model (RSM) Studies (Source: Tables 4.8 and 4.8a)
Early Decline Stage with 16 years of actual production performance
Depletion Stage
Cumulative Production
MMSTB
399.0
399.0
399.0
399.0
399.0
399.0
(An indication of Project Maturity)
% OOIP
27.8%
26.2%
23.0%
27.8%
26.2%
23.0%
MMSTB
Original Oil In-Place (OIIP)
1,434
1,525
1,739
1,434
1,525
1,739
MMSTB
Recoverable Oil (EUR)
573
686
869
674
793
991
Implied Recovery Factor (%OIIP) % OOIP
40%
45%
50%
47%
52%
57%
MMSTB
Oil Reserves
174
287
470
275
394
592
Production Performance Trend (PPT) Analysis (Source: Tables 4.10 and 4.10a)
Late Decline Stage with 26 years of actual production performance
Depletion Stage
Cumulative Production
MMSTB
518.9
518.9
518.9
518.9
518.9
518.9
(An indication of Project Maturity)
% OOIP
34.0%
34.0%
34.0%
34.0%
34.0%
34.0%
MMSTB
Original Oil In-Place (OIIP)
1,525
1,525
1,525
1,525
1,525
1,525
MMSTB
Recoverable Oil (EUR)
702
747
824
778
824
900
Implied Recovery Factor (%OIIP) % OOIP
46%
49%
54%
51%
54%
59%
MMSTB
Oil Reserves
183
228
305
259
305
381
Table 4.12—Assessment of Contingent Resources
Estimates of Project OIIPs and EURs During Production Phase
Bases and Estimates by Resource Category
(under a Planned CO2 Miscible Project)
Assessment Method
Units
Low
Best
High
Material Balance (MB) and Analogous Methods (Source: Table 4.6)
8 years of production performance under Peripheral Waterflood
Depletion Stage: Early Production Stage
and results of one analog CO 2 Pilot.
Cumulative Production
MMSTB
220.8
220.8
220.8
(An indication of Project Maturity)
% OOIP
17.0%
13.8%
11.0%
MMSTB
Original Oil In-Place (OIIP)
1,300
1,600
2,000
Recovery Factor (%OIIP)
% OOIP
5%
10%
15%
MMSTB
Recoverable Oil (EUR)
65
160
300
Reservoir Simulation Model (RSM) and Analogous Methods (Source: Table 4.8b)
16 years of production performance under Peripheral Waterflood
Depletion Stage: Early Decline Stage
and the results of two analog CO 2 Pilots (only one fully realized).
Cumulative Production
MMSTB
399.0
399.0
399.0
(An indication of Project Maturity)
% OOIP
27.8%
26.2%
23.0%
MMSTB
Original Oil In-Place (OIIP)
1,434
1,525
1,739
Recovery Factor (%OIIP)
% OOIP
15%
18%
22%
MMSTB
Recoverable Oil (EUR)
215
275
382
Single OIIP Estimate and Analogous Methods (Source: Table 4.10b)
26 years of production performance under Peripheral Waterflood
Depletion Stage: Late Decline Period
and fully realized results of two analog CO 2 Pilots.
Cumulative Production
MMSTB
518.9
518.9
518.9
(An indication of Project Maturity)
% OOIP
34.0%
34.0%
34.0%
MMSTB
Original Oil In-Place (OIIP)
1,525
1,525
1,525
Recovery Factor (%OIIP)
% OOIP
13%
16%
20%
MMSTB
Recoverable Oil (EUR)
198
244
305
Assessment of Petroleum Resources Using Deterministic Procedures
75
As a concluding remark, it may be beneficial to reiterate the commonly practiced
development and production strategy for projects with long-life reserves similar to our example
oil project. Because of the availability of many development opportunities in excess of their
development needs, oil reservoirs have been developed at relatively low annual depletion rates
from 2 to 5% of EUR initially by many Middle East producers. That is why the full reservoir
development (drilling of all well-spacing units) typically requires 20 to 30 years to complete, and
extends the economic lives beyond 100 years. Having the leverage to practice a low reservoir
depletion strategy and continuous drilling to maintain the initially established plateau production
rate as long as possible provides significant benefits including the opportunity to take better
advantage of new technological advancements to maximize the ultimate recovery and keep the
unit development and production costs at significantly lower levels than those prevalent
elsewhere.
1.
2.
3.
4.
5.
Key takeaways from this chapter are as follows:
Petroleum resources assessment is and must be a continuous ongoing technical process
supported by good practices and collaborative efforts across many disciplines.
Petroleum resources assessment should use the methods most suitable for analyzing the data
available, including static geoscientific and engineering as well as dynamic actual production
performance, and be carried out by a collaborative multidisciplinary team of expert
evaluators consisting of geoscientists and engineers.
Assessment of subsurface petroleum resources is complex and subject to many uncertainties
in static and dynamic reservoir parameters coupled with regulatory, operational and
economic uncertainties. Although exceptions will continue to exist, the quantity of reliable
data and degree of certainty in the estimates of PIIP and EUR are expected to increase over
time.
Irrespective of project maturity and the amount and quality of performance data available, the
degree of certainty in resource estimates largely depends on the ability of experienced
reserves evaluation professionals not only to know the most appropriate methods to use, but
also to exercise prudent judgment, ensuring the reasonableness and validity of these
estimates by always comparing them with those estimated using different methods and/or
with the known analog reservoirs.
Use of the full PRMS classification and categorization matrix provides a standardized
framework for characterizing the estimates of marketable hydrocarbon volumes according to
their associated risks and uncertainties.
References
Arps, J.J.1945. Analysis of Decline Curves. Trans., AIME, 160, 228.
Aziz, K. and Settari, A. 1979. Petroleum Reservoir Simulation. New York: Elsevier Publishers.
Boberg, T.C. 1988. Thermal Oil Recovery, An Exxon Monograph. New York: John Wiley &
Sons.
Brons, F. 1963. On the Use and Misuse of Production Decline Curves. Producers Monthly
(September 1963): 22–25.
Canadian Oil and Gas Evaluation Handbook, first edition, vol. 2. The Petroleum Society of the
Canadian Institute of Mining, Metallurgy and Petroleum, Calgary Section.
Carcoana, A. 1992. Applied Enhanced Oil Recovery. Englewood Cliffs, New Jersey: Prentice
Hall.
Guidelines for Application of the Petroleum Resources Management System
76
Craig, F.F. 1971. The Reservoir Engineering Aspects of Waterflooding. Monograph Series, SPE,
Richardson, Texas 3.
Cronquist, C. 2001. Estimation and Classification of Reserves of Crude Oil, Natural Gas, and
Condensate. Richardson: Texas.
Dake, L.P. 1978. Fundamentals of Reservoir Engineering. New York: Elsevier.
Dake, L.P. 2001. The Practice of Reservoir Engineering, revised edition. Developments in
Petroleum Science, Elsevier, New York 36.
Donaldson, E.C., Chilingarian, G.V., and Yen, T.F. 1985. Enhanced Oil Recovery I–
Fundamentals and Analyses. Developments in Petroleum Science, Elsevier, New York 17A.
EPRCO (Exxon Production Research Company). 1982. From an EXXON in-house Reservoir
Engineering School attended by the author in 1982.
Ershaghi, I. and Abdassah, D. 1984. A Prediction Technique for Immiscible Processes Using
Field Performance Data. J Pet Technol 36 (4): 664–670; Trans., AIME, 277. SPE-10068-PA.
DOI: 10.2118/10068-PA.
Ershaghi, I. and Omoregie, O. 1978. A Method of Extrapolation of Cut vs. Recovery Curves. J
Pet Technol 30 (2): 203–204. SPE-6977-PA. DOI: 10.2118/6977-PA.
Ertekin, T., Abou-Kassem, J.H., and King, G.R. 2001. Basic Applied Reservoir Simulation.
Textbook Series, SPE, Richardson, Texas 7.
Fanchi, J.R. 2006. Principles of Applied Reservoir Simulation, third edition. New York: Elsevier.
Fekete Associates. 2008. Rate Transient Analysis Pamphlet, http://www.fekete.com.
Fetkovich, M.J., Vienot, M.E., Bradley, M.D., and Kiesow, U.G. 1987. Decline Curve Analysis
Using Type Curves: Case Histories. SPE Form Eval 2 (4): 637–656. Trans., AIME, 283.
SPE-13169-PA. DOI: 10.2118/13169-PA.
Fetkovich, M.J.1980. Decline Curve Analysis Using Type Curves. J Pet Technol 32 (6): 1065–
1077. SPE-4629-PA. DOI: 10.2118/4629-PA.
Green, D.W. and Willhite, G.P. 1998. Enhanced Oil Recovery. Textbook Series, SPE,
Richardson, Texas 6.
Harrell, R., Hodgin, J., and Wagenhofer, T. 2004. Oil and Gas Reserves Estimates: Recurring
Mistakes and Errors. Paper SPE 91069 presented at the SPE Annual Technical Conference
and Exhibition, Houston, 26–29 October. DOI: 10.2118/91069-MS.
Havlena, D. and Odeh, A.S. 1963. The Material Balance as an Equation of a Straight Line. J Pet
Technol 15 (8): 896–900. Trans., AIME, 228. SPE-559-PA. DOI: 10.2118/559-PA.
Havlena, D. and Odeh, A.S.1964. The Material Balance as an Equation of a Straight Line–Part
II, Field Cases. J Pet Technol 16 (7): 815–822; Trans., AIME, 231. SPE-869-PA. DOI:
10.2118/869-PA.
Hsieh, F.S., Vega, C., and Vega, L. 2001. Reserves Estimation Using PC Decline Analysis. J
Can Pet Technol 40 (11): 13–15. 01-11-TN2. DOI: 10.2118/01-11-TN2.
Klins, M.A. 1984. Carbon Dioxide Flooding: Basic Mechanics and Project Design. Boston:
IHDC.
Lake, L.W. 1989. Enhanced Oil Recovery. Eaglewood Cliffs, New Jersey: Prentice Hall.
Latil, M. 1980. Enhanced Oil Recovery. Houston: Gulf Publishing.
Lee, J. and Wattenbarger, R.A. 1996. Gas Reservoir Engineering. Textbook Series, SPE,
Richardson, Texas 5.
Long, D.R. and Davis, M.J. 1988. A New Approach to Hyperbolic Curve. J Pet Technol 40 (7):
909–912. SPE-16237-PA. DOI: 10.2118/16237-PA.
Assessment of Petroleum Resources Using Deterministic Procedures
77
Mannon, R.W. and Porter, R.L. 1989. Decline Curve Analysis Using Computerized Type
Curves. SPE Comp App (November-December 1989) 13.
Mattar, L. and Anderson, D. 2005. Dynamic Material Balance. Paper 2005-113 presented at the
Petroleum Society’s 6th Canadian International Petroleum Conference (56th Annual
Technical Meeting), Calgary, 7–9 June.
Mattar, L. and McNeil, R. 1998. Flowing Gas Material Balance Method. J Can Pet Technol 37
(2). Paper 98-02-06. DOI: 10.2118/98-02-06.
Mattax, C.C. and Dalton, L.D. 1990. Reservoir Simulation. Monograph Series, SPE, Richardson,
Texas 13.
Petroleum Resources Management System (PRMS). 2007. SPE. http://www.spe.org/spesite/spe/spe/industry/reserves/Petroleum_Resources_Management_System_2007.pdf.
Prats, R. 1982. Thermal Recovery. Monograph Series, Henry L. Doherty Series, SPE,
Richardson, Texas 7.
PS-CIM: Canadian Oil and Gas Evaluation Handbook, Volume 1, second edition .2007.Calgary
Section: Petroleum Society of the Canadian Institute of Mining, Metallurgy and Petroleum.
PS-CIM: Canadian Oil and Gas Evaluation Handbook, Volume 2, first edition .2005. Calgary
Section: Petroleum Society of the Canadian Institute of Mining, Metallurgy and Petroleum.
PS-CIM: Determination of Oil and Gas Reserves, second edition. 2004. Monograph No. 1,
Calgary Section: Petroleum Society of the Canadian Institute of Mining, Metallurgy and
Petroleum.
Purvis, R.A. 1985. Analysis of Production-Performance Graphs. J Can Pet Technol 24 (4): 44.
PETSOC-85-04-03. DOI: 10.2118/85-04-03.
Sandrea, R. and Nielsen, R. 1974. Dynamics of Petroleum Reservoirs Under Gas Injection.
Houston: Gulf Publishing.
Smith, C.R. 1966. Mechanics of Secondary Oil Recovery. Malabar, Florida: Robert E. Krieger
Publishing.
Stalkup, F.I. 1983. Miscible Displacement. Monograph Series, Henry L. Doherty Series, SPE,
Richardson, Texas 8.
Taber, J.J. and Martin, F.D. 1983. Technical Screening Guides for the Enhanced Recovery of
Oil. Paper SPE 12069 presented at the SPE Annual Technical Conference and Exhibition,
San Francisco, 5–8 October. SPE-12069-PA. DOI: 10.2118/12069-PA.
Tearpock, D.J. and Bischke, R.E. 1991. Applied Subsurface Geological Mapping. Englewood
Cliffs, New Jersey: Prentice-Hall.
Thiele, M.R. 2010. Streamline Simulation for Modern Reservoir Engineering Workflows. J Pet
Technol 62 (1): 64–70. SPE-118608-MS. DOI: 10.2118/118608-MS.
Towler, B.F. 2002. Fundamental Principles of Reservoir Engineering. Textbook Series, SPE,
Richardson, Texas 8.
van Everdingen, A.F. and Hurst, W. 1949. The Application of the Laplace Transformation to
Flow Problems in Reservoirs. Trans., AIME, 186, 305–324.
Walsh, M.P. and Lake, L.W. 2003. A Generalized Approach to Primary Hydrocarbon Recovery.
Handbook of Petroleum Exploration and Production Series, Elsevier, New York 4.
Wang, X. 2010. Developer of the geological maps (cross-sections and net pay isochores) and
estimates of reservoir rock volumes for the Example Petroleum Project, Resources
Assessment Department, Exploration, Saudi Aramco, Dhahran, Saudi Aramco (May 2010).
Guidelines for Application of the Petroleum Resources Management System
78
Chapter 5
Probabilistic Reserves Estimation
Wim J.A.M. Swinkels
5.1 Introduction
Understanding and managing the range of uncertainty in reserves and resources estimation are
important aspects of the business of exploration and production of oil and gas. Oil and gas
professionals want to capture this uncertainty in order to
• Make development plans that can cover the range of possible outcomes
• Provide a range of production forecasts to evaluate the expected outcome of their ventures
• Measure exploration, appraisal, and commercial risks
• Ensure that they can handle an unfavorable outcome (i.e., that they have an economic project,
even if the low case materializes)
• Understand and communicate the confidence level of their reserves estimate
Approaches to handle uncertainty in resource estimates can be seen on a scale from
completely deterministic to fully probabilistic as follows:
1. The Deterministic Method—A single value is used for each parameter, resulting in a single
value for the resource or reserves estimate. The estimated volumes can be classified as
Proved, Probable, or Possible in the incremental approach, or 1P, 2P, or 3P in the cumulative
approach described in the PRMS, depending on the level of uncertainty. Each of these
categories can be related to specific areas or volumes in the reservoir.
2. The Scenario Method (sometimes called Realizations Method)—This is essentially an
extension of the Deterministic Method. In this case, a range of possible deterministic
outcomes or scenarios is described. Usually, this collection of scenarios is then translated
into a pseudoprobability curve. The scenario method combines elements of the deterministic
approach and of the full probabilistic method.
3. The Probabilistic Method—The statistical uncertainty of individual reservoir parameters is
used to calculate the statistical uncertainty of the in-place and recoverable resource volumes.
Often a stochastic (e.g., Monte Carlo) method is applied to generate probability functions by
randomly sampling input distributions. Such functions lend themselves readily to various
quantitative risk analysis and decision-making methods. Probability levels of the total
recoverable volume can then be related to 1P, 2P, and 3P reserve categories, or the
corresponding resources categories, using the Petroleum Resources Management System
(PRMS) guidelines (SPE 2007). In many cases, there is no one-to-one relation between one
of these outcomes and a physical volume or area in the reservoir.
This chapter focuses on the last two of these three approaches, which both have a
probabilistic nature, as opposed to the first approach, which is deterministic. Increasingly,
industry and regulatory bodies are accepting the use of these methods; see for example, the
modernized US Securities and Exchange Commission rules (US SEC 2008).
Probabilistic Reserves Estimation
79
The value of the probabilistic and scenario methods in the business process is that
• Both describe the full range of uncertainty and reveal upsides and downsides
• They easily allow calculation of the value of information of various activities
• Both allow calculation of effects of interdependent uncertainties
• They provide a good interface with decision support and financial modeling methods
• Both methods can easily be applied across the boundary between exploration and production
activities
We will briefly describe the deterministic method, then we will discuss the scenario
approach, and finally we will address issues in the application of probabilistic methods.
5.2 Deterministic Method
The deterministic method uses a single value for each parameter, based on a well-defined
description of the reservoir, resulting in a single value for the resource or reserves estimate.
Typically, three deterministic cases are developed to represent either low estimate (1P or 1C),
best estimate (2P or 2C), or high estimate (3P or 3C), or Proved, Probable, and Possible
estimates. Each of these categories can be related to specific areas or volumes in the reservoir
and a specific development plan.
Advantages of the deterministic method are
• The method describes a specific physical case; physically inconsistent combinations of
parameter values can be spotted and removed.
• The method is direct, easy to explain, and manpower efficient.
• The estimate is reproducible.
• Because of the last two advantages, investors and shareholders like this method, and it is
widely used to report Proved Reserves for regulatory purposes.
A feature and potential weakness of the deterministic method is that it handles each reserves
category in isolation and does not quantify the likelihood of the mid, high, and low case.
5.3 Scenario Method
The scenario method describes a range of possible outcomes for the reservoir, which are
consistent with the observed data. A single, physically consistent outcome within this range with
its estimated in-place volume is called a subsurface realization. For the purpose of obtaining a
recovery factor, we can then define a development scenario for each subsurface realization and
subsequently book recoverable volumes in the appropriate PRMS categories. The collection of
scenarios can also be translated into a pseudoprobability curve by assigning associated chances
of occurrence. This method combines elements of the deterministic approach and of the full
probabilistic method.
Multiple realizations of the subsurface should be
• Based on ranked uncertainties. For this purpose we first have to specify and rank the main
uncertainties.
• Internally consistent (i.e., a realization should consist of parameter values or sets of
conditions that can physically exist together).
• Associated with a probability of occurrence (but not necessarily equally probable).
• Related to a technically sound development option.
When using PRMS, the Proved Reserves are a high-confidence commercial case within the
set of scenarios (i.e., a realization that results in a reserves number at the low end of the range).
Guidelines for Application of the Petroleum Resources Management System
80
The scenario method can also be used with each branch representing an individual
simulation run (history-matched, if production history exists). By assigning probabilities to these
branches, it is possible to define appropriate low (1P or 1C), best (2P or 2C), and high (3P or 3C)
estimates from the set of simulation runs. Because this is not strictly a probabilistic method, it is
not necessary to select outcomes at precisely the probability equivalents of these categories.
Various methods are available to represent and visualize a set of realizations. The two most
important ones are the probability-tree method and the use of scenario matrices.
5.3.1 Probability-Tree Representation of the Scenario Method. When using probability trees
to represent scenarios, each branch in the tree represents a set of discrete estimates and
associated probability of occurrence, as shown in the relatively simple example in Fig. 5.1.
Structure
GWC
0.6
-3190
por*ntg
Net
Pore
Volume
0.6
0.14
479
94
0.18
0.4
0.23
787
155
0.12
0.6
0.14
234
46
0.12
0.4
0.23
384
75
0.08
0.6
0.14
350
69
0.18
0.4
0.23
575
113
0.12
0.6
0.14
181
36
0.12
0.4
0.23
297
58
0.08
GRV
GIIP
10**9 m3
GIIP
Probability
3420
high
0.5
0.4
0.6
-3040
-3190
1670
2500
0.5
low
0.4
-3040
1290
Probabilities in italics
Fig. 5.1—Probability-tree example. (GRV=gross rock volume; GWC=gas/water contact).
Each end branch in this tree is the result of a possible route along the branching points in the
tree and hence represents a specific subsurface realization, for which an in-place volume [gas
initially in place (GIIP) in this case] can be calculated. The example shows that the branches are
associated with different probabilities, and thus a combined probability can be calculated for
each endpoint. By combining the endpoint GIIP values and their cumulative probabilities, this
tree also can be used to generate a cumulative probability curve, which is provided in Fig 5.2, for
the example in Fig. 5.1. In this curve, the 90, 50, and 10% probability values can be easily
identified. In this example, a GIIP estimate of about 40 x 109 m3 has a probability of 90% to be
exceeded.
Obviously such a tree can straightforwardly handle dependencies between probabilities on
the branching points.
Probabilistic Reserves Estimation
81
1
Cumulative Probability
0.8
0.6
0.4
0.2
0
0
50
100
150
200
GIIP (10**9 m3)
Fig. 5.2—Cumulative probability density function (PDF) constructed from probability tree.
5.3.2 Matrix Representation of the Scenario Method. The realization matrices method to
represent subsurface realizations and development concepts is more qualitative but often richer
in content than the probability-tree method described above. The example in Fig. 5.3, modified
from a recent project, shows various reservoir aspects that are represented by columns. Each cell
in the columns describes a possible outcome. A realization is the consistent combination of a set
of possible outcomes. The example also shows that realizations can be described according to a
specific theme (e.g., in this case a “High-STOIIP/Low-Drainage” case is represented by triangles
in the diagram, while the hexagons represent a scenario characterized by high residual saturation,
strong aquifer, and low drainage).
Fractures
Direction
Intensity
Fracture
Structure(F
Perm
lanks)
M
H (Less
steep)
L
L
L
w/wet
L?
Pmax=Pi
M (Type-A
dips
everywhere)
M
M
M
mixed
M?
Pmax=1.2
Pi
L (Steeper
dips)
H
H
H
oil/wet
H?
Pmax=1.5*
Pi
Oil Sat
N/G
Matrix
Perm
Wettability
GOR
Cap Rock
Integrity
Realisation:
1: High
STOIIP/High
Drainage rate
2: High STOIIP/
Low Drainage
Isotropic
(uniform)
High
(one/2m)
Medium
(one/20m)
3: High Sor /
Strong Aquifer /
Low Drainage
preferential
Low
orientation (one/100m)
infinite
Figure 5.3—Example of scenario method.
Guidelines for Application of the Petroleum Resources Management System
82
The scenario matrix is useful for generating scenarios that cover a wide range of possible
outcomes and hence can play an important role in project-framing exercises. This representation
does not allow as much quantitative treatment of probabilities as the scenario tree method. For
an example see O’Dell and Lamers (2005).
5.3.3 Strengths and Weaknesses. The scenario method combines the strengths of probabilistic
(stochastic sampling) and deterministic approaches. Its strong points are
• It allows generation of subsurface realizations made up of consistent sets of parameters.
• It is a useful approach to identify development concepts.
• Development concepts can be tested against all possible reservoir outcomes.
• It can be helpful in defining targets for appraisal (through value-of-information analysis).
• It provides an auditable method to identify the selected reserves or resources category
outcomes.
A weakness of the scenario method is the limited number of scenarios that can usually be
handled, with the risk of undersampling the range of possibilities. Assigning a probability to each
scenario relies heavily on geological and petroleum engineering judgment. Both of these
shortcomings are sometimes tackled by using experimental design methods, as described by Al
Salhi et al. (2005).
5.4 Probabilistic Method
In the probabilistic method, we use the full range of values that could reasonably occur for each
unknown parameter (from the geosciences and engineering data) to generate a full range of
possible outcomes for the resource volume. To do this, we identify the parameters that make up
the reserves estimate and then determine a so-called probability density function (PDF). The
PDF describes the uncertainty around each individual parameter based on geoscience and
engineering data. Using a stochastic sampling procedure, we then randomly draw a value for
each parameter to calculate a recoverable or in-place [e.g., stock-tank oil initially in place
(STOIIP)] resource estimate. By repeating this process a sufficient number of times, a PDF for
the STOIIP or recoverable volumes can be created. This Monte Carlo procedure is schematically
shown in Fig. 5.4.
p
p
p
p
p
φ
1/Bo
Sh
p
N/G
GRV
STOIIP
p>x
STOIIP
Fig. 5.4—Monte Carlo approach to volumetrics.
Probabilistic Reserves Estimation
83
Dependencies between parameters often exist and must be represented in the probabilistic
estimation of recoverable volumes. Commonly encountered positive correlations are between
net-to-gross gas saturation and porosity in clastic reservoirs. An obvious negative correlation
exists between the oil and gas volumes in a gas-capped oil reservoir. It should be noted that the
resultant PDF for the recoverable resources is often asymmetrical.
It is important to remove physically impossible realizations from the model because they
will inappropriately skew the range of outcomes. A good practice is to select a realization that
represents a “typical” 1P or 2P case and to supplement each probabilistic assessment with
discrete realizations for the low, mid, and high cases. This ensures that one is clear about the
development scenario that the probabilistic estimate represents and should guard against
allowing unrealistic cases into the assessment. It should be noted that probabilistic estimates for
an accumulation will differ depending on the development scenario selected.
For fields where production data exists, the workflow includes the additional step of history
matching. A result of this workflow is a group of equally probable history-matched models
created by a combination of parameters, using for instance genetic algorithms and evolutionary
strategy to match the production history.
5.4.1 Volumetric Parameters and Their Uncertainty Distribution. Uncertainty in volumetric
estimates of petroleum reserves and resources is associated with every parameter in the
equations.
Gross Rock Volume (GRV). Usually, the most important contribution to overall uncertainty
is in the GRV of the reservoir—just how big is it? This uncertainty may be related to
• Lack of definition of reservoir limits from seismic data
• Time-to-depth conversion in seismic observations
• Dips of the top of the formation
• Existence and position of faults
• Whether the faults are sealing to hydrocarbon migration and production
The GRV depends critically on the height of the hydrocarbon column because the volume of
a reservoir anticline increases roughly proportionally with the cube of the column. Typical
reporting requirements (US SEC 2008) for Proved Reserves recognize this sensitivity by limiting
the rock volume to that above the lowest known hydrocarbons (LKH) unless otherwise indicated
by definitive geosciences, engineering, or performance data.
Rock Properties: Net-to-Gross and Porosity. The uncertainty associated with the properties
of the reservoir rock originates from the variability in the rock. It is determined through
petrophysical evaluation, core measurements, seismic response, and their interpretation. While
petrophysical logs and measurements in the laboratory may be quite accurate, the samples
collected may be representative only for limited portions of the formations under analysis. A
core 4 in. wide is not necessarily a representative sample of a buried and altered river delta,
superimposed plains of meandering river channels, a suite of beach deposits, turbid marine
landslides, or other geological formations. Only in rare instances can precise measurements of
porosity, net-to-gross ratio, fluid saturation, and factors affecting fluid flow be applied directly
and with confidence. For the most part, they help to condition one or several alternative
(uncertain) interpretations.
Guidelines for Application of the Petroleum Resources Management System
84
Fluid Properties. For fluid properties, a few well-chosen samples may provide a
representative selection of the fluids. The processes of convection and diffusion over geologic
times have generally ensured a measure of chemical equilibrium and homogeneity within the
reservoir, although sometimes gradients in fluid composition are observed.
Sampling and analysis may be a significant source of uncertainty. Reservoirs with initial
gradients in fluid composition or where phase changes have occurred will be affected by
production. Here, samples may be unrepresentative of the initial fluids and they may be
misinterpreted easily. Hence, fluid definition under such conditions is less certain than in virgin
reservoirs. Additionally, sampling may be affected by acquisition methodology, such as
recombination procedures in surface sampling, and fluid properties may also be impacted by
other factors, such as storage, which can alter original reservoir conditions.
Recovery Factor (RF). Recovery is based on the execution of a project and affected by the
shape and the internal geology of the reservoir, its properties and fluid contents, and the
development strategy. If a reservoir can be described in sufficient detail, then numerical models
can be made of the effects of well and drainage-point density and location, fluid displacement,
pressure depletion, and their associated production and injection profiles. Realistic alternatives,
conditioned by available information and consistent with the definitions, may be modeled to
assess the uncertainties. If a reservoir is poorly defined, material balance calculations or analog
methods may be used to arrive at an estimate of the range of RFs. Uncertainty ranges in the RF
can often be based on a sensitivity analysis. Selecting Distribution Functions for Individual Parameters. In probabilistic resource
calculations, it is the task of the estimator to specify a PDF that fits the information available.
Modern tools (such as spreadsheet-based or other commercially available statistical software)
allow for a wide choice of PDFs (normal, log-normal, triangular, Poisson, etc.).
The following offers some practical guidance on the selection of the parameter distributions:
• Make a conscious decision on range and shape of the input distributions for the volumetric
calculation on the basis of direct reservoir and geoscience information or appropriate
analogs.
• The distributions must be applied only in the range for which they usefully reflect the
underlying uncertainty. Avoid distributions that extend into infinity. Ensure that distributions
do not become negative or exceed unity for parameters expressed as fractions or ratios, such
as porosity, net-to-gross, saturation, or recovery efficiency.
• The most generic PDFs to describe the uncertainty of the mean are normal and log-normal
distributions. Their disadvantage is the infinite tail, which can lead to unrealistic scenarios.
One solution is to apply truncation at meaningful values; however, if truncation significantly
impacts the overall shape of the PDF, then it is probably more appropriate to use another
PDF as the starting point.
• Recall that the range of values required is that which represents the evaluator’s uncertainty in
the value of the mean, rather than the distribution of the data itself.
• Do not confuse the three measures of centrality (expectation or mean, mode, and median)
when defining the distribution.
• Be aware of what the low and high value estimates represent: extremes (such as minima and
maxima (P100/P0) or some other probability value (such as P95/P05, P90/P10, etc.).
• The PDF of a sum of log-normal distributions tends toward a normal distribution. As a result,
a product of independent factors, whose logarithms are of the same magnitude, tends toward
Probabilistic Reserves Estimation
•
•
85
a log-normal distribution. Examples of entities that are strongly affected by products are the
reserves of an accumulation and the permeability of a porous system.
The PDF of the sum of a large number of independent quantities of the same magnitude tends
toward a normal distribution. Examples are the reserves of a large number of equally sized
fields in a portfolio and the porosity of a rock body.
If the independent quantities are not of the same magnitude, the sum and its PDF will be
dominated by the largest ones.
Many practitioners approximate PDFs with triangular distributions, particularly when data
are limited and the range is narrow. In cases where a probability distribution cannot be
determined easily, a uniform distribution is sometimes used. Such distributions may be
considered coarse approximations of reality. However, uncertainty ranges of the resulting
volumes are more influenced by mean values and standard deviations than they are by the shape
of the distributions of the individual parameters that make up the estimate.
The most common error when working with poorly defined quantities is to underestimate the
possible uncertainty range of each parameter. Particular attention should therefore be paid to this,
regardless of the distributions chosen. As a general principle, the less the information, the wider
the range. It should be emphasized that distributions to be used in a probabilistic analysis routine,
even if measured data are available, should properly describe the uncertainty of the specific input
parameters being represented. For example, the porosity distribution from core or logs is
conceptually different from the distribution of the average porosity in the reservoir. Therefore,
the use of existing data distributions as observed in the existing wells is not valid. Fig. 5.5
illustrates an example. In this example some of the core plugs have 0% porosity. Obviously 0%
porosity cannot be used as the low value in the distribution of average fieldwide porosity if it is
known that average porosity is always above zero. A further discussion of the differences
between distributions of the raw data and of the reservoir average is provided in Cronquist
(2001).
Distribution of
Zonal Porosity Averages
Distribution of
Porosity Data Points
0
0.10
0.20
Porosity (fraction bv)
Fig. 5.5—Frequency distributions of porosity.
The known distribution of available data should be considered only as the starting point to
define the PDF for reservoir parameters. If cutoffs are applied to the reservoir parameters (e.g., if
net sand has a 5% porosity cutoff), then these should be reflected in the reservoir parameter PDF.
Guidelines for Application of the Petroleum Resources Management System
86
If abundant data are available (e.g., computer analyses of the porosity logs), and the
geologic processes of sedimentation, deformation, and diagenesis are such that the variability
along the hole is representative of the variability in the reservoir, then the actual distribution of
these data, after cutoffs, can be used as a starting point. If only scarce data are at hand, then the
range should be defined and turned into a distribution. Always keep in mind that the distribution
function should describe the distribution of the reservoir-averaged parameter value. Table 5.1
provides typical ranges of uncertainty in the most common reservoir parameters.
TABLE 5.1—SOME RESERVOIR PARAMETERS AND TYPICAL RANGES OF UNCERTAINTY
Range
Source
GRV
+/– 30%
Net-to-Gross
Porosity from logs
Porosity from cores
Hydrocarbon saturation
Dip
+/– 20%
+/– 15%
+/– 10%
+/– 20%
+/– 10%
+/– 30%
+/– 5%
3D Seismic
2D Seismic
Well logs
Logs
Cores
Well logs
Dipmeter
Seismic
PVT test
Formation volume factor (Bo or Bg)
Note: Ranges are a percentage of the actual measurement, not e.g., porosity percentage points.
Warning: The values in this table are typical ranges provided to use for comparison with your actual parameter
ranges. Do not use as default uncertainty ranges.
5.4.2 Performance Methods: Parameters and Their Uncertainty Distribution. When
sufficient production performance information is available, reserves can be assessed by using
performance-based methods, such as decline curve analysis (DCA). In classical DCA, the
uncertainty in the estimated ultimate recovery is mainly caused by the selected decline model
(exponential, hyperbolic, or harmonic) and the selected matching or regression period.
A possible approach to arrive at a probabilistic estimate using performance-based methods is
by using the hyperbolic decline equation:
q=
qi
1
(1+ bd i t )b
and matching on the hyperbolic decline constant b, as well as the initial nominal decline rate di.
Since exponential decline (b=0) and harmonic decline (b=1) are limiting cases of the hyperbolic
decline, this eliminates the problem of selecting a decline model. By varying b, di, and the
matching period within reasonable limits, a distribution for the resulting ultimate recovery can be
obtained, from which Proved, Probable, and Possible Reserves can be derived. Other approaches
have been explored (Cheng et al. 2005).
Probabilistic Reserves Estimation
87
Combining Risk and Uncertainty. PDFs resulting from the methods described previously
can be combined with risk factors, which will result in typical shapes for different situations on
both sides of the exploration/production boundary. Fig. 5.6 shows cumulative risked PDFs for
resources in five different situations. First of all, there are four curves that intersect the y-axis at
a value below one. For these cases, there is a finite probability that the STOIIP is 0 (i.e., these
curves describe prospects for which it is not certain that they contain oil). The intersection point
with the y-axis is the probability of success (PoS), as used in exploration situations. The curve
that intersects the y-axis at Probability 1, describes a discovered oil accumulation, with a range
of uncertainty and PoS=1. In more detail, the figure shows the following:
• Relatively poor prospect; volume is small and PoS is also limited.
• Speculative prospect; small probability of a large volume.
• Either/or prospect; in case of success there is a relatively well-defined volume.
• Small confident prospect; PoS is relatively large, mean volume in the success case is in the
order of 30 million bbl.
• Discovery; in this example case the P10, or the upside, is almost twice the P50, the P90 value
is some 60% of the P50.
1.0
Very poor prospect
Probability
Speculative prospect
Either/Or prospect
Small, confident prospect
Discovery
0.5
0
0
50
100
150
STOIIP (million m3)
Fig. 5.6—PDFs.
5.4.3 Strengths and Weaknesses. Strengths of the probabilistic method include
• The uncertainty range of the result can be derived from basic parameter uncertainty ranges
• Easily lends itself to numerical treatment
• Can be applied throughout the business cycle from exploration to production
• Naturally links in with value-of-information work
• Allows capture of the range of outcomes when insufficient detailed data are available
Weaknesses, on the other hand, are
• Can lead to extensive, complicated, and sometimes ineffective calculation work
• Categories (e.g., P90, P50, P10) may not correspond to specific physical areas or volumes
when simple Monte Carlo methods are used. In cases where geological and simulation
Guidelines for Application of the Petroleum Resources Management System
•
•
88
models are used to do the analysis, the models and parameters used for the P90, P50, and P10
scenarios can be identified.
The PDF of basic parameters is not always known and technical judgment has to be applied
Dependencies between parameters are even more difficult to assess.
5.5 Practical Applications
The probabilistic approach to resource estimation can be applied usefully to other economic and
engineering tasks, such as resource categorization, experimental design, and value-ofinformation calculations.
5.5.1 Resource Categorization. Under PRMS, when the range of uncertainty in recoverable
volumes is represented by a probability distribution, then low, best, and high estimates are
defined as follows:
• There should be at least a 90% probability (P90) that the quantities actually recovered will
equal or exceed the low estimate [Proved (1P) for Reserves, 1C for Contingent Resources].
• There should be at least a 50% probability (P50) that the quantities actually recovered will
equal or exceed the best estimate [Proved + Probable (2P) for Reserves, 2C for Contingent
Resources].
• There should be at least a 10% probability (P10) that the quantities actually recovered will
equal or exceed the high estimate [Proved + Probable + Possible (3P) for Reserves, 3C for
Contingent Resources].
Although the most probable value of the distribution is the mode, common industry practice (as
described in the PRMS) is to use the median (P50) as the best technical estimate for a single
entity (reservoir or zone).
5.5.2 Experimental Design. Experimental design is a well-known set of statistical methods that
are helpful in generating the scenarios or cases required to efficiently cover all possible outcomes
of the reservoir or field development at hand. Steps in the evaluation typically include the
following:
1. Define the set of parameters and their ranges.
2. Perform a sensitivity analysis and select the parameters that have the most impact on the
result.
3. Calculate the reserves for a limited number of realizations of the model. These realizations
are based on combinations of parameters determined by an experimental design procedure.
4. Use the results of this limited number of model runs to generate a so-called response
function, or response surface, using regression techniques.
5. Use the PDFs of the input parameters to generate the PDF of the response function in a
stochastic sampling (e.g., Monte Carlo) process.
Experimental design is particularly useful when the analysis is based on performance data, such
as material balance or reservoir simulation. A description of this method is provided in van Elk
et al. (2000), and an illustrative example is described by Al Salhi et al. (2005).
Probabilistic Reserves Estimation
89
5.5.3 Value of Information. The goal of appraisal is to reduce uncertainty, and it is necessary to
address the value of the additional information gained against cost. In the appraisal example
represented by Fig. 5.7, the curve for the STOIIP estimation has a gentle slope before appraisal,
indicating a wide distribution of possible values. After appraisal, the slope is much steeper,
indicating that the range of possible answers has been narrowed. Even if the outcome is
unfavorable (i.e., the post-appraisal curve is below the economic minimum), the appraisal
activity has delivered value by preventing unnecessary investments. A post-appraisal curve that
is in the economic realm will allow for a more focused development.
This narrowing of possible answers allows the design of a more cost-effective development,
provided that the post-appraisal range of STOIIP exceeds some economic threshold. The
increased cost-effectiveness of the development is the value of the information (VoI) gained by
the appraisal. As long as the appraisal cost is lower than this VoI, further appraisal is necessary.
Cumulative probability
1.0
economic
minimum
post
appraisal
0.5
before
appraisal
post
appraisal
0
0
50
100
150
STOIIP (million m3)
Fig. 5.7—Value of information.
References
Al Salhi, M.S., Van Rijen, M.F., Alias, Z.A., Visser, F., Dujk, H., Lee, H.M., Timmerman, R.H.,
Upadhyaya, A.A., and Wei, L. 2005. Reservoir Modelling for Redevelopment of a Giant
Fractured Carbonate Field, Oman: Experimental Design for Uncertainty Management and
Production Forecasting. Paper IPTC 10537 presented at the International Petroleum
Technology Conference, Doha, Qatar, 21–23 November. IPTC-10537-MS. DOI:
10.2523/10537-MS.
Cheng, Y., Wang, Y., McVay, D.A., and Lee, W.J. 2005. Practical Application of a Probabilistic
Approach to Estimate Reserves Using Production Decline Data. Paper SPE 95974 presented
at the SPE Annual Technical Conference and Exhibition, Dallas, 9–12 October. DOI:
10.2118/95974-MS.
Cronquist, C. 2001. Estimation and Classification of Reserves of Crude Oil, Natural Gas, and
Condensate, Chap 22. Richardson, Texas: SPE.
Modernization of Oil and Gas Reporting. US Securities and Exchange Commission (2008).
Guidelines for Application of the Petroleum Resources Management System
90
O’Dell, M. and Lamers, E. 2005. Subsurface Uncertainty Management and Development
Optimization in the Harweel Cluster, South Oman. SPE Res Eval & Eng 8 (2): 164–168.
SPE-89110-PA. DOI: 10.2118/89110-PA.
Petroleum Resources Management System (PRMS). 2007. SPE. http://www.spe.org/spesite/spe/spe/industry/reserves/Petroleum_Resources_Management_System_2007.pdf.
van Elk, J.F., Guerrera, L., and Gupta, R. 2000. Improved Uncertainty Management in Field
Development Studies through the Application of the Experimental Design Method to the
Multiple Realisations Approach. Paper SPE 64462 presented at the 2000 SPE Asia Pacific
Oil and Gas Conference and Exhibition, Brisbane, Australia, 16–18 October. DOI:
10.2118/64462-MS.
Definitions and Rules
Probability
Probability Density
Function (PDF)
Cumulative PDF (Cdf);
Survival Function (Sf)
Measures of Centrality
The extent to which an event is likely to occur measured by the ratio of the number of
occurrences to the whole number of cases possible. 5
Note that the probability used in reserves estimation is a subjective probability, quantifying
the likelihood of a predicted outcome.
Probability as a function of one or more variables, such as a hydrocarbon volume.
To each possible value of a variable, a Cdf (Sf) assigns a probability that the variable does
not exceed (does exceed) that value.
The “SPE/WPC Petroleum Reserves Definitions” use survival function in the statement: “If
probabilistic methods are used, there should be at least 90% probability that the quantities
actually recovered will equal or exceed the estimate.”
The three measures of centrality defined below coincide only when PDFs are symmetrical.
This is seldom the case for reserves. In general, and for most practical purposes, they
differ.
The mean is also known as the expectation or the expected value. It is the average value
over the entire probability range, weighted with the probability of occurrence.
n
Mean, Expectation, or
Expected Value
Mean = ∑ xi ⋅ P( xi ) or ∫ x ⋅ P( x) ⋅ d ( x)
Mode, or Most
Probable Value
where x = reserve value and P(x) = probability of x.
The mean of statistical distributions can be added arithmetically in aggregation
The mode is the most probable value. It is the reserves quantity where the PDF has its
maximum value.
The value for which the probability that the outcome will be higher is equal to the
probability that it will be lower.
Median (also known
as P50)
Percentiles
P90
P50, or Median
P10
i =1
Measures of Dispersion
The quantity for which there is a certain probability, quoted as a percentage, that
quantities actually recovered will equal or exceed the estimate.
The quantity for which there is a 90% probability that the quantities actually recovered
equal or exceed the estimate. In reserves estimation, this is the number quoted as
proven value.
The quantity for which there is a 50% probability that the quantities actually recovered
equal or exceed the estimate.
The quantity for which there is a 10% probability that the quantities actually recovered
equal or exceed the estimate.
The variance is calculated by adding the square of the difference between values in
distribution and the mean value and calculating the arithmetic average:
s
5
∑ (x − μ ) = (x − μ)
=
∫
n
n
Variance
2
New Concise Oxford Dictionary
i =1
i
i
b
a
2
f ( x)dx
the
will
the
will
will
the
Probabilistic Reserves Estimation
Standard Deviation
91
where x = reserve, μ = mean, and f(x) = PDF.
It is convenient to square the differences because this avoids the cancelling of positive and
negative values. The same effect may be obtained by taking absolute values of the
difference, but the mathematical properties of such a measure are not as elegant as those
of the variance.
Describes the spread of a variable around its mean value. It is defined as the square root
of the variance.
Guidelines for Application of the Petroleum Resources Management System
92
Chapter 6
Aggregation of Reserves
Wim J.A.M. Swinkels
6.1 Introduction
In reserves and resources estimation, estimates are based on performance evaluations and/or
volumetric calculations for individual reservoirs or portions of reservoirs. These estimates are
summed to arrive at estimates for fields, properties, and projects. The uncertainty of the
individual estimates at each of these aggregation levels may differ widely, depending on
geological setting and maturity of the resource. This cumulative summation process is usually
referred to as “aggregation” (SPE 2007).
Adding up estimates, or ranges of estimates, with such different levels of uncertainty can be
impacted by the purpose for which the estimate is required.
Oil companies, considering long-term performance of their assets, will use the “best
estimate” of the volumes for investment purposes; this generally is based on the sum of Proved
plus Probable (2P) volumes. They work on the assumption that in the long run, the portfolio of
their best estimates will be realized, with the downside in one case compensated for by the
upside in another situation. However, it is best practice that reserve estimates always be reported
as a range (1P/2P/3P or, in the case of Contingent Resources, 1C/2C/3C). Where assessments are
based on deterministic methods, summations are arithmetic and by category. Where probabilistic
assessments are available, companies may aggregate probabilistically to the
field/property/project level, but subsequent summations are generally arithmetic. For internal
portfolio analyses, companies may use fully probabilistic methods, with risking applied where
appropriate.
Investors, accountants, and utilities will usually require a high level of certainty and
concentrate on the Proved (1P) volumes, or to a lesser extent, the Proved plus Probable (2P)
volumes. Gas contracts are typically based on Proved Reserves, which adds a strong business
incentive to the accurate determination (and summation) of Proved Reserves. Long-term gas
contracting is sometimes based on Proved plus Probable Reserves where there is a large gas
resource that is most economically developed over the life of the gas contract.
Accountants may use the ratio of production to Proved Developed Reserves or other
reserves categories as the basis for depreciating or depleting the cost of acquiring and developing
reserves over time as the reserves are produced. In some areas, the ratio of production to Proved
plus Probable Reserves (including any Undeveloped Reserves) is used as the basis for
depreciation. Depreciating the cost of investments has an impact on business profits and
indicators as return on average capital employed (ROACE). For these calculations, accountants
require the reserves to be assessed at the level at which the investments apply.
Thus, reported aggregates of reserves and resources not only encompass variations in
associated uncertainties, but also require a detailed portfolio cash flow analysis to understand the
value they represent.
Aggregation of Reserves
93
Sec. 6.2 addresses some general technical issues in reserves aggregation. The discussion on
the aggregation of reserves also addresses the issue that the uncertainty of the sum of volumes
will be less than the sum of the uncertainties of the individual volumes. In other words, the
uncertainty decreases with an increasing number of independent units available. The implications
of the resulting uncertainty reduction in a diverse portfolio, also called the portfolio effect, will
be discussed in Sec. 6.3.
Sec. 6.4 discusses aggregation over reserves categories, and the use of scenario methods for
reserves aggregation is shown in Sec. 6.5, followed in Sec. 6.6 by a few notes on normalization
and standardization of volumes. Sec. 6.7 summarizes the chapter in a few simple guidelines.
6.2 Aggregating Over Reserves Levels (Wells, Reservoirs, Fields, Companies, Countries)
6.2.1 Reservoir Performance. The best estimate of ultimate recovery (EUR) can be derived
through volumetric methods or through extrapolation of well performance in mature fields [e.g.,
by decline curve analysis (DCA)]. In applying DCA methods, good industry practice is to work
from the lowest aggregation level (e.g., wells or completions) upwards, comparing both
individual and reservoir- or field-level analysis. Performance extrapolation at the reservoir level
can lead to a higher EUR than the sum of the extrapolated well decline curves for that reservoir
for many reasons. A summation of individual-well-level DCA may not adequately address
catastrophic failures, such as wellbore or completion damage. Also, the comparison of
individual-well DCAs to a field-level DCA will highlight small, systematic biases that could
otherwise be undetectable at the low level of anaylsis.
One reason for this may be that aggregating from individual-well decline curves does not
capture the effect that shutting in a well can sometimes give, an extra economic life to the
surviving wells in the reservoir. Another problem, which is specific to gas fields, is that the p/z
plot per well often does not properly reflect the overall reservoir pressure decline. In such
situations, it is good practice to use an overall reservoir performance extrapolation if possible.
This effect is aggravated if we use a 1P estimate for the well extrapolations. If we sum the
individual well results into a reservoir-level estimate, then we assume full dependence (i.e., that
all wells will develop their low case simultaneously). There always will be some dependency for
wells in the same reservoir because they have the same geological formation, drive mechanism,
mode of production, etc., but disregarding the fact that the well results have some statistical
independence may result in overly conservative estimates at the reservoir level for the sum of
high confidence estimates.
Two approaches have been proposed to avoid the effect of arriving at too low aggregates for
P1 (or C1) volumes when adding low cases:
1. Apply decline analysis at the reservoir level.
2. Statisticaly add Proved estimates from well level to reservoir level.
Method 1: Performance Extrapolation and DCA at the Reservoir Level. The first approach,
performance extrapolation at the reservoir level is, along with the individual well DCAs, an
obvious and necessary supporting part of the performance analysis. In cases where reliable
production data at the well level are not available, DCA analysis at a higher level of aggregation
(e.g., platform, plant, production station, or reservoir) may be the only basis for the performance
extrapolation. Another condition that calls for a higher-level DCA is the occurrence of strong
interference effects between neighboring wells.
Performance extrapolation at the reservoir level has a number of pitfalls:
Guidelines for Application of the Petroleum Resources Management System
•
94
The performance will include the effects of ongoing drilling, development, and maintenance
activities.
• The aggregate may include wells at different stages of decline, with different GORs, etc.
• It has been shown that for multiwell aggregates, the decline will be dominated by the highrate wells, which may lead to over- or underestimation of the reserves.
Discussion of these issues in DCA are provided by Harrell et al. (2004) and Purves in his chapter
(PS-CIM 1994) on DCA methods.
Method 2: Statistical Aggregation of Well-Level Proved Estimates. Another approach to
compensate for arithmetic addition of high-confidence estimates may be to apply a form of
statistical addition. This has other pitfalls:
• Well-level Proved estimates are often mutually dependent because of common aquifers,
formation heterogeneity, facilities, operation constraints, etc. If independence is assumed, it
is up to the reserves evaluator to justify this assumption.
• The proposed methods often rely on statistical simplifications (e.g., the assumption of
normal distributions for the reserves estimates).
It should be noted that the above problems are avoided when using simulation models to capture
reservoir performance. However, often DCA is the method of choice because of its independence
from various modeling assumptions.
6.2.2 Correlations Between Estimates. One of the major reasons why summation of reserves,
particularly Proved Reserves, sometimes leads to complications is that many parameters in the
reserves calculation are dependent upon each other. This leads to further dependencies between
individual reserves estimates for reservoir blocks, reservoirs, or subreservoirs, such that low
reserves in one reservoir element will naturally be associated with low reserves in another one, or
just the opposite. There are numerous reasons for dependency between reservoirs of a geological
(fault location, contact height), methodological (similar interpretation methods), or personal
(same optimistic geologist for a number of reservoirs) nature, as classified in Table 6.1.
Rigorous methods for evaluating measures of dependency and correlation matrices are
discussed in van Elk, Gupta, and Wann (2008).
An example of a positive relation between two estimates can be illustrated with the area-vs.depth plot of a field shown in Fig. 6.1, which consists of two reservoir sands divided by a shale
layer. The sands have a common oil/water contact (OWC). Obviously, in this case, the reserves
for both sands will change in the same direction if an exploration well finds the OWC somewhat
shallower or if a new seismic interpretation lifts the flank of the structure. Adding up the low or
Proved values for the two sands is justified to arrive at an estimate for a low reserves case for the
field.
Aggregation of Reserves
95
TABLE 6.1—CAUSES OF DEPENDENCE BETWEEN RESERVES ESTIMATES OF
FIELDS OR RESERVOIRS
Type of Dependence
_____Example of Situation/Parameter____
None
Local, independent pressure systems
No shared risk identified (fully
independent).
Common seismic survey or seismic interpreter
Weak
Common source of recovery factor estimates, tools (e.g., reservoir simulator), and
A shared risk is not considered to
ranges
be important when compared to
Saturation-calculation method (e.g., Waxman Smits, Archie)
other, known, independent risks.
Saturation-height function (e.g., using capillary-pressure data from other fields)
Medium
The shared risks could be real and
significant.
The success of a low-pressure compression project in one field is a prerequisite of
success in another, and hence the recovery factor estimates are potentially linked.
However, the major components of the uncertainties in reserves of the two fields
(structure, etc.) remain independent.
Strong
The shared risks are known to be
real and significant.
The aquifer and pressure systems between two adjacent fields are likely to be
common, and actions in one field will affect recovery in the others.
Total
The shared risks are absolute.
Two adjacent oil accumulations have commonality assumed in all essential risks
(reservoir unit, velocity model, aquifer drive); thus, their reserves estimates should be
added arithmetically.
Negative
The shared risks are absolute and
inverse.
An oil field is developed in a core area only. Additional upside in stock-tank oil initially in
place (STOIIP) in flank areas will result in a reduction in the average recovery factor.
Uncertainty in fault location works in the opposite direction for gross rock volume (GRV)
in two adjacent blocks.
Modified from Carter and Morales (1998).
West Star Field - Area vs. depth
-2850
-2900
Top layer 1
Base Layer 1
-2950
Top layer 2
Depth (mss)
-3000
Base layer 2
-3050
-3100
-3150
Oil Water Contact
-3200
-3250
-3300
0
10
20
30
40
50
60
Area (10**6 m3)
Fig. 6.1—West Star field area vs. depth.
Obviously, in this case, the reserves for both sands will change in the same direction if an
exploration wells finds that the common OWC is somewhat shallower or if a new seismic
interpretation lifts the flank of the structure. Summing the low or Proved values for the two sands
is justified to arrive at an estimate for a Proved Reserves case for the field.
A negative correlation occurs when there is uncertainty about the location of a fault between
two noncommunicating reservoir blocks. An example is a reservoir with two blocks, A and B,
Guidelines for Application of the Petroleum Resources Management System
96
separated by a fault. There is an uncertainty of several hundreds of meters in the fault location.
The impact of this uncertainty can be represented by a relation between the fault position and the
GRV of the two blocks, Blocks A and B, as Fig. 6.2 illustrates.
Figure 6.2—GRV of Fault Block A as a Function of Fault Position
3.00E+09
3
GRV (m )
2.50E+09
2.00E+09
1.50E+09
1.00E+09
4
4.2
4.4
4.6
4.8
5
5.2
5.4
5.6
5.8
6
Fault Position (km)
Calculating the gas initially in place (GIIP) is now possible in both blocks; obviously, there
is a negative correlation between the volume in one block and the volume in the other. If we now
add up the Proved values in each of the two blocks, we are adding two low cases, which in
reality will never occur simultaneously. It is clear that, in this case, the Proved value of the two
blocks combined will be larger than the arithmetic sum of the two Proved values.
A probabilistic picture of this situation is given in Fig. 6.3, which shows the cumulative
probability curves of Blocks A and B. The figure also shows the arithmetic sum of the two
blocks (curve Block A + Block B) compared with the actual distribution of the full reservoir. The
sum of the Proved values of the two blocks at the 90% level is some 7x109 m3 (0.245 Tcf), or
11% less than the Proved value at the 90% level derived for the full reservoir.
Aggregation of Reserves
97
Figure 6.3—Probability Distribution—Reservoir Blocks A and B
100%
Reservoir distribution
Cumulative Probability
80%
Block A + Block B
60%
Block A
40%
Block B
20%
0%
0
20
40
60
80
100
120
3
GIIP (mrd m )
Another commonly encountered negative correlation is the situation in an oil reservoir with
a gas cap, where solution gas below the gas/oil contact (GOC) is estimated separately. If there is
an uncertainty in the GOC depth, then there is a negative correlation between the gas reserves
that are carried above and below the GOC. [There is also, of course, a negative correlation
between oil reserves and gas-cap reserves. Unless information is available, such as detailed fluid
properties, to guide the placement of the GOC, it is usually appropriate to assume that the
volume above the highest known oil is occupied by the lower-value product (usually gas).]
Adding up the best estimate, or 2P, values makes good sense to arrive at the combined value
of total GIIP, being the sum of free gas and solution gas. Obviously, this is not the case for the
Proved Reserves because the low case for free gas will correspond with a high case for solution
gas and vice versa. To handle this, a stochastic procedure (using a spreadsheet add-in such as
Crystal BallTM or @RiskTM, for example) can be used to arrive at the resultant distributions for
GIIP and reserves at the field level.
6.2.3 Levels of Aggregation. As discussed above, summation of Proved Reserves in a statistical
way will often result in different volumes than the straightforward “bookkeeping” arithmetic
summation. Theoretically, the probabilistic summation can go up to the highest levels of
aggregation. Many companies and organizations now appear comfortable with the idea of adding
probabilistically up to the field level for specific purposes, provided dependencies are handled
properly.
The PRMS (SPE 2007) recommends that reserves figures should not incorporate statistical
aggregation beyond the field, property, or project level, an approach that has been followed by
others in the industry (SEC 2008).
A field containing different reservoir blocks (layers, pools, accumulations) can be fiscally
ring fenced and developed as one unit. Fiscal unit-of-production depreciation of the assets is then
defined at this level. Above this level of aggregation, statistical summation may lead to fiscal
problems. For that reason, there is much less industrywide acceptance for statistical treatment of
aggregation above the field level and up to company or regional level. Probabilistic summation
at these higher aggregation levels may be of interest only to the small group of professionals
involved in portfolio management in the larger companies.
Guidelines for Application of the Petroleum Resources Management System
98
It should be noted that if only deterministic estimates are available, the only option is to use
arithmetic summation. The discussion of statistical aggregation only applies if we have a
probabilistic analysis (or convert scenarios to quantitative probabilities).
6.3 Adding Proved Reserves
6.3.1 Pitfalls of Using Arithmetic (Dependent) Addition of Proved Reserves. If we quote
Proved Reserves, we commonly refer to volumes that are “estimated with reasonable certainty to
be commercially recoverable” in the development of the field. In probabilistic reserves
estimation methods, PRMS interprets reasonable certainty as a 90% probability (P90) of meeting
or exceeding the quoted value (SPE 2007). The Proved Reserves represent a high-confidence
(i.e., relatively conservative) estimate of the recoverable resources; for this reason, it is widely
used by investors and bankers. In dealing with only a single asset, this makes sense because it
allows for the risk that the development may result in much less than the expected hydrocarbon
recovery.
Whenever oil investors or companies add Proved Reserves of several reservoirs
arithmetically, they underestimate the aggregated value of their assets. This is because the
upsides on most reserves estimates will more than compensate for the downsides on the 10%
underperforming assets in the portfolio. This will certainly happen if the estimates of the
volumes are independent of each other. For this reason, most companies will rely more on the 2P
numbers than on the high-confidence 1P estimates for business planning purposes.
In daily life, we are aware of this when we try to spread our risks and avoid, for example,
putting all our investments in one particular asset. For instance, a company committing a number
of gas fields to a contract seems unnecessarily conservative in assuming that, ultimately, each
field will produce only its initially estimated Proved volume or less. If the reserves estimates are
independent, then the upsides in one field may offset a disappointing outcome in others. In other
words, the P90 of the total is certainly higher than the (arithmetic) sum of the P90 volumes of the
individual fields [see also Schuyler (1998)]. For the same reason, arithmetic addition of the 3P
values of individual reservoirs will overestimate the real upside of the combined asset.
If we stick to arithmetic aggregation of Proved Reserves, we run the risk of systematically
underestimating the value of our combined assets. Technically, this can be avoided because tools
are readily available to account for the favorable condition of having a mix of assets. In addition,
it is sometimes possible to convince the investing community (and some governments) to value a
combination of assets higher than the sum of the Proved volumes of the individual parts.
Organizations that have a portfolio of very diverse resources will naturally be interested in
accounting for the uncertainty reduction that is caused by the diversity of their portfolios. This
may be true for larger oil and gas companies as well as for governments. Aggregates derived in
this way are outside the scope of the PRMS and other classification systems.
Governments of some countries around the North Sea, such as Norway and the Netherlands,
add the national Proved Reserves in a probabilistic way to account for the independent nature of
these volumes. For instance, the Dutch Ministry of Economic Affairs has applied the method of
probabilistic summation for Proved Reserves since the mid-1980s. In 1996, it stated in its annual
report on Dutch exploration and production activities: “The result of applying the method of
probabilistic summation is that the total figure obtained for the Proved reserves now indeed
represents the Proved proportion of total Dutch reserves in a statistically more valid manner.”
Aggregation of Reserves
99
6.3.2 Arithmetic or Dependent Summation. Arithmetic summation is the usual straightforward
way of adding volumes and thus of aggregating reserves. Let us look at two gas-bearing reservoir
blocks, A and B, with the dimensions in Table 6.2.
TABLE 6.2—EXAMPLE CASE: GAS RESERVOIRS A AND B
__Block A__
__Block B__
__Total__
1.74
1.16
2.9
Porosity
0.22
0.22
0.22
Net-to-gross
0.85
0.85
0.85
Saturation
0.8
0.8
0.8
Gas expansion
205
205
205
9
10 m
Total GRV
Expectation of GIIP
10 m
9
3
53.4
35.6
89.0
9
3
43.3
28.5
71.8
10 m
Proved GIIP
3
With the range and PDF of these paramters, we can construct a probability distribution of
the individual blocks as shown in Fig. 6.4, with the cumulative probability of exceeding a given
volume on the vertical axis.
Figure 6.4—Probability Distribution Reservoir—Blocks A and B
100%
80%
Block
A
Block
B
60%
40%
20%
0%
0
10
20
30
40
50
GIIP (mrd
60
70
80
90
100
m 3)
Note that for the sum of the Proved Reserves in Table 6.2, we have taken the arithmetic sum
of two Proved numbers, both of which have a 90% probability of being met or exceeded. In fact,
by adding these, we assume complete dependency between the two cases; i.e., we assume that if
the low side of one case materializes, the same thing will happen with the other case. In this way,
we arrive at a potentially pessimistic number for the Proved GIIP, representing the situation that
both blocks turn out to be relatively disappointing. However, this could well be the case if both
Guidelines for Application of the Petroleum Resources Management System
100
blocks have a common gas/water contact (GWC), or if their volumes are determined by the same
seismic phenomena, as shown in one of the examples in the previous section. Even the bias
introduced by the same subsurface team, applying the same methods, working on two reservoir
blocks may introduce a positive correlation.
6.3.3 Probabilistic or Independent Summation. If the reservoir volumes of the two blocks are
deemed to be truly independent of each other, we can still calculate the sum of the mean 6 values
by straightforward summation. However, if we now derive the Proved value from the
distribution of the sum, we may have situations (e.g., in a Monte Carlo simulation of this case)
where a low outcome of Block A will be combined with a high outcome of Block B, or the other
way around. What happens in practice is that optimistic outcomes in one block compensate for
the disappointing outcomes in the other block. This results in a cumulative distribution curve for
the combined GIIP that is steeper (i.e., has a smaller spread) than the curve for the arithmetically
added volumes, as shown in Fig. 6.5. This tendency of the uncertainty range to narrow is a
statistical phenomenon that will always be observed if we stochastically add up quantities that
have independent statistical distributions.
Applying this approach and making the assumption of complete independence, we can state
with 90% certainty that there is at least 77x109 m3 of gas in both reservoir blocks, as opposed to
72x109 m3 of gas using arithmetic summation. In situations where gas contracts are based on
Proved Reserves, this may have considerable business implications.
Figure 6.5—Arithmetic and Probabilistic Addition, A and B
1
Cumulative Probability
Arithmetic addition
0.8
Arithmetic Proven
72 mrd m3
Probabilistic
addition
0.6
0.4
Probabilistic Proven
77 mrd m3
0.2
0
50
60
70
80
90
100
110
120
130
140
GIIP (mrd m3)
Methods to aggregate volumes independently (assuming no correlation between possible high
and low outcomes) are
• Scenario trees, representing the possible outcomes as branches of a tree and calculating the
overall outcome. This method is treated in Sec. 6.5.
• Monte Carlo methods, using a spreadsheet add-in (such as @RiskTM or Crystal BallTM).
• Treating the volume estimates as a physical measurement with an associated error and then
using error propagation methods.
6
The mean is used in this discussion as it is the only statistical function that is correctly additive across distributions.
However, it should be recalled that the definitional “best estimate” case is represented by the median 2P (P50)
number.
Aggregation of Reserves
101
In the last mentioned method, we approach the uncertainty of the estimate for a reservoir
volume by ΔI = Mean − Proved. We can then calculate the uncertainty for the sum of Reservoirs
A and B using the relation Δ 21+ 2 = Δ 21 + Δ 2 2 .
This method is an approximation that holds only for symmetric distributions, but it has the
strong advantage of being easy to calculate. It is very suitable for estimating an upper limit for
the effects of probabilistic summation. We have to be aware, however, that volumetric estimates,
being the product of a number of parameters, tend to be log-normally distributed (i.e.,
asymmetrical and with a tail of high values).
6.3.4 The Intermediate Case—Using Correlation Matrices. In the previous section, we
discussed fully dependent, or arithmetic, summation and fully independent, or probabilistic,
summation of Proved Reserves. Most practical situations will be in between these two extreme
cases. The reason for this is that some parameters of our estimates will be correlated, while
others will be completely independent of each other. Ignoring correlation in these cases will lead
to overestimation of Proved Reserves. The rigorous solution in this situation is to calculate
probability distributions, specify the correlation between them, and generate the resulting
probability distribution for the aggregate. Monte Carlo simulation is the obvious method to
achieve this. The overriding problem in this approach is the proper specification of the
correlation matrix.
An interesting approach to this problem, illustrated with a real-life example, is presented by
Carter and Morales (1998). They describe the probabilistic summation of gas reserves for a
major gas development project consisting of 25 fields sharing common production facilities.
Each field has a range of gas reserves, expressed at the P90 (Proved), P50, P10, and expectation
(mean) levels. The Proved Reserves per field are defined as the volume that has a 90% chance of
being met or exceeded. Adding these volumes arithmetically results in a volume of Proved
Reserves across the project that is 15% lower than the stochastically combined P90. Because
neither full dependence nor full independence can be assumed, the authors then proceed to
analyze the areas of potential dependence between the individual estimates by applying the
following procedure:
1. The areas of dependence are tabulated for individual fields to identify common factors
between fields. These areas include technical, methodological, and natural subsurface
commonalities between the GIIP estimates of the fields. Commonality is classified as weak,
medium, or strong.
2. An estimate of correlation coefficients is made by assigning values of 0.1, 0.3, and 0.5 for a
weak, medium, or strong dependence and combining them into an array suitable for use in a
Monte Carlo presentation.
3. The reserves distribution (for each field) as defined by the P90, P50, and P10 confidence
levels is expressed as a double-triangular PDF.
4. A matrix of correlation coefficients is used to describe the shared risks between fields, with
a coefficient for each pair of fields varying from 0 (fully independent) to ± 1 (fully
dependent).
5. The reserves distributions for each field are then probabilistically summed up over the
project using the previously defined correlation matrices in the @RiskTM add-in within an
ExcelTM spreadsheet.
The result of applying this method for the case described was that the gas reserves at the
90% confidence level are some 9% greater than those resulting from arithmetic summation. Not
Guidelines for Application of the Petroleum Resources Management System
102
taking the dependencies between the fields into account, the increase would have been 15% over
the straightforward arithmetic summation.
Some common-sense measures are described that make the process more practical. The first
of these is that fields with the highest level of dependence were added arithmetically into field
groups. This ensures a conservative bias in the approach and reduces the size of the correlation
matrices to 15 field groups. High dependence occurs between adjacent gas fields believed to be
in pressure communication, or between new gas developments sharing structural risk.
Another important measure is a peer review on the semiquantitative process of assigning
dependencies. The emphasis in this review process is on identifying factors (such as volumetric
uncertainty) that cause full or almost full independence, even if other strong links (such as a
shared aquifer) can be demonstrated.
A third simplification of the process was that negative correlation coefficients were
disregarded in the analysis. It is possible that a correlation coefficient between two fields can be
negative. While in principle both positive and negative dependencies can be handled, only
positive dependencies were identified for the project fields. It was considered during the peer
review process that use of a negative coefficient might unduly narrow the range of uncertainty in
the final aggregation.
The linked risks resulting from shared surface facilities and constraints are also excluded
from the analysis. They are considered to be common (project) risks, and problems with facilities
are considered surmountable if they materialize. This type of shared risk can be included in the
analysis, if required.
The authors investigated the robustness of their method by changing the dependencies. The
result of this sensitivity case supported the general observation that in this type of analysis, the
outcome is not very sensitive to changes in individual correlation coefficients.
Use of correlation matrices as described above is similar to other reserve estimation methods
in two important aspects:
•
•
The figures used are subjective and change when new insights are gained. However, in view
of the large number of interrelations (dependencies/independencies) of the fields, major
reversals of opinion must occur to change the overall result by a significant amount.
As the established risks are addressed in more detail, specific correlation coefficients will be
updated with the proper audit trail. For example, a new seismic interpretation by a new team
may result in the dependencies in seismic interpretation being removed after the new
interpretation has been accepted.
6.4 Aggregating Over Resource Classes
To achieve business growth and reserves replacement objectives, oil companies identify
hydrocarbon volumes in their acreage and execute appraisal and development plans to turn these
into Developed Reserves and ultimately into production. To this end, they review EUR targets
for existing and newly discovered fields as well as for untested opportunities and identify which
activity—exploration, appraisal, development, further study, or new technology development—is
required to achieve these targets. As explained in Chap. 2, various classes of resource volumes
can be defined in this process.
The volumes thus identified may or may not be ultimately produced, depending on the
success of the project. For this reason, it is important not to aggregate Reserves, Contingent
Resources, and Prospective Resources “without due consideration of the significant differences
Aggregation of Reserves
103
in the criteria associated with the classification” 7 that comprise the risk of accumulations not
achieving commercial production. In general, this means that the different resources classes
should not be included into an aggregate volume. However, a common practice to assess a total
portfolio of assets is the use of “risked volumes” calculated by multiplying mean success
volumes (MSVs) by the probability of success (POS). POS includes both geological chances
(presence of hydrocarbons) and probability of commercial development. This is usually deemed
to be applicable for a large portfolio of independent projects.
In adding up such volumes, a meaningful total can be defined only by adding the risked
volumes (POS x MSV) resulting in a statistical expectation of the recovery. This will be no
problem for a large portfolio of opportunities or for a smaller portfolio where the discounted
volumes do not add significantly to the total. Naturally, the range of uncertainty of the aggregate
will increase if more speculative categories of resources are included. If such an approach is
taken, it is strongly recommended that the resource class components are identified separately
and not to report just one single number.
Where many risked volumes are being added, the scenario tree may become a required
approach to looking at discrete combinations of possible outcomes; scenario trees are discussed
in the next section.
6.5 Scenario Methods
6.5.1 Example of Low Dependence Between Reservoir Elements. A powerful approach to
aggregate reserves is the use of scenario methods. To illustrate this approach we discuss two
examples: one where we add volumes with a low degree of dependence and one where we
aggregate highly correlated volumes.
In the first case, we evaluate three sands (M, N, and S), for which the reservoir parameters
and GRVs are relatively independent. The reason for this independence is that the reservoirs
occur in different geological formations at very different depths, so there are few factors that
cause low and high cases of the sands to coincide. Table 6.3 gives low, median, and high
STOIIP for the sands.
TABLE 6.3—STOIIP UNCERTAINTY RANGE OF THREE OIL-BEARING SANDS
__Volumes__ 7
__Low__
_Median_
_High_
M-sands
17
23
30
Mean=
Expectation
23.3
N-sands
29
41
54
41.3
S-sands
10
15
25
16.7
PRMS Sec. 4.2.1.1
Guidelines for Application of the Petroleum Resources Management System
104
To construct a scenario tree for this situation, we have taken the low, median, and high values of
STOIIP with equal probability in the sands with the largest volume, the N-sands. We then
combine these first with the M-sands and subsequently with the S-sands. This results in a
scenario tree with 27 end branches (Fig. 6.6).
Scenario Approach
Low Degree of Dependency
45%
MReserves
35%
20%
Low
33 %
25%
N-Reserves
Median
33 %
Mreserves
50%
25%
SReserves
41%
34%
25%
6%
5%
4%
56
61
71
SReserves
39%
34%
27%
5%
4%
3%
62
67
77
SReserves
37%
34%
29%
2%
2%
2%
69
74
84
SReserves
35%
34%
31%
3%
3%
3%
68
73
83
SReserves
33%
34%
33%
5%
6%
5%
74
79
89
SReserves
31%
34%
35%
3%
3%
3%
81
86
96
SReserves
29%
34%
37%
2%
2%
2%
81
86
96
SReserves
27%
34%
39%
3%
4%
5%
87
92
102
SReserves
25%
34%
41%
4%
5%
6%
94
99
109
High
33 %
20%
MReserves
35%
45%
Fig. 6.6—Scenario Tree.
As can be seen in Fig. 6.6, there is some correlation between the occurrence of low, high, and
median cases for each of the sands (i.e., the probability that the M-sands have a high value are
higher than if the N-sands are high, etc.). At the end branches, we can read off the total STOIIP
in each of the 27 possible combinations of N-, M-, and S-sands, as well as the frequency of
occurrence.
It is important to note that the low values in this example are not the same as the Proved
values for the sands because they are not the 90% probability point in the cumulative probability
curve. The probability of the branches and the dependencies between these probabilities, as
represented in the tree, should reflect the understanding of the geological processes at work. The
Aggregation of Reserves
105
resulting STOIIP distribution can then be used as a building block for a resource assessment in
PRMS. A plot of these figures is provided in Sec. 6.5.3.
6.5.2 Example of Dependent Reservoir Elements. In this second example, the sands are on top
of each other in a single geological structure; thus, they are all impacted by the same uncertainty
in structural dip and the location of the bounding faults. This is a case with high dependencies
between the sand volumes because a high volume in the N-sands will increase the likelihood of a
high volume in the other sands. We assume that geological parameters, such as porosity or netto-gross pay play a secondary role and disregard them to keep the number of branches limited.
Fig. 6.7 shows the scenario tree for this case.
High Degree of Dependency
67%
MReserves
30%
3%
Low
33 %
15%
NReserves
Median
33 %
MReserves
70%
15%
SReserves
80%
20%
0%
18%
4%
0%
56
61
71
SReserves
65%
30%
5%
6%
3%
0%
62
67
77
SReserves
50%
45%
5%
0%
0%
0%
69
74
84
SReserves
32%
60%
8%
2%
3%
0%
68
73
83
SReserves
10%
80%
10%
2%
19%
2%
74
79
89
SReserves
8%
60%
32%
0%
3%
2%
81
86
96
SReserves
5%
45%
50%
0%
0%
0%
81
86
96
SReserves
5%
30%
65%
0%
3%
6%
87
92
102
SReserves
0%
20%
80%
0%
4%
18%
94
99
109
High
33 %
3%
MReserves
30%
67%
Fig. 6.7—Scenario tree with high degree of dependent reservoir elements.
In the scenario tree in Fig 6.7, the dependency between the three sands shows up as a higher
probability that high sand volumes are combined with high volumes. A low case in one sand will
tend to go together with a low case in another sand. A plot of these figures is provided in Sec.
6.5.3.
Guidelines for Application of the Petroleum Resources Management System
106
6.5.3 Comparing Degrees of Dependence. We can go through the same exercise with a similar
scenario tree for full independence. This is a straightforward extension from the previous two
examples, with the chance factors on the branches of the tree all taken to be one-third (33%). By
using the results of the scenario trees, we can construct the pseudoprobability curves for each of
the three cases by sorting and calculating cumulative probabilities. Fig. 6.8 shows the results.
This analysis now results in the summations of the three sands shown in Table 6.4.
Figure 6.8—Cumulative Probability Curve
1.00
0.90
Low dependency
High dependency
Cumulative Probability
0.80
Full independence
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
40
50
60
70
80
90
100
110
120
Oil Volume
TABLE 6.4—PROBABILISTIC ADDITION WITH VARYING
DEGREES OF DEPENDENCY, STOIIP
Expectation =
___Mean___
P85 = Low
P50 = Median
P15 = High
M-sands
17
23
30
23.3
N-sands
29
41
54
41.3
S-sands
10
15
25
16.7
Independent sum
67
81
96
81.3
Low-dependence sum
64
81
98
81.2
High-dependence sum
59
79
105
81.1
Fully dependent addition
(arithmetic)
56
79
109
81.3
As expected, the mean values are hardly affected by the assumptions used in the four
aggregation procedures. Because the distributions used are almost symmetric, there is also little
variation in the value of the median case. For the low and the high values taken at the 15% and
85% levels, respectively, there are some clear differences.
Aggregation of Reserves
107
The fully independent case and the low-dependency case closely resemble each other in the
cumulative probability representation. As expected, the fully independent case results in a
narrower range of volumes than the low-dependency case. Apparently, the result is not very
sensitive to the chance factors in the scenario tree.
6.5.4 Comparing Scenario Trees and Correlation Methods. We now have discussed two
methods for handling dependencies in aggregating volumes: the use of matrices to describe
correlation between parameters (in Sec. 6.3) and the construction of scenario trees in this section.
Table 6.5 compares the two methods.
TABLE 6.5—COMPARISON BETWEEN SCENARIO TREE AND CORRELATION MATRIX METHODS
Scenario Trees
Correlation Matrices
Natural link with decision making
Easy link with probabilistic description—allows
Monte Carlo approach
Dependencies made visible in the diagram
Dependencies shown in matrices
Conditionality depends on ordering of branches—
needs care to construct the tree
Dependencies independent of ordering
Not practical with large number of parameters
Many correlated parameters can be handled
Intuitively clear
Less intuitive/more abstract
The ease of use and the link with decision-making approaches generally will make the
scenario tree method the preferred choice.
6.6 Normalization and Standardization of Volumes
Hydrocarbon volumes can only be added and properly interpreted only if there is no doubt of
their meaning. On a global basis, there may be variations in specifications so that for
aggregations to be meaningful, we need to normalize volumes. Under PRMS, reserves and
resources are measured at the custody transfer point at pressure and temperature, for which
agreed values are used. This may lead to small differences between reported volumes in different
unit-of-measurement systems. The commonly used reporting conditions for oil and natural-gasliquid (NGL) field volumes and for fiscalized sales volumes are standard conditions [m3 or bbl at
15°C, 1 atm (760 mm Hg); m3 or bbl at 60°F, 14.7 psia). Local deviations from this convention
exist where sales gas is measured and reported in other units.
For gas, we can apply two standardization steps:
1. Conversion to standard pressure and temperature conditions. Unfortunately, various
combinations of pressure and temperature in field units as well as SI units are in current use.
The pressure and temperature conversion factors for gas are, to some extent, dependent on
gas composition, and slightly different values may be used.
2. Conversion to a volume with an equivalent heating value. Heating value conversion factors:
Field gas is usually reported at the composition and heating value it has at the wellhead, and
usually at standard conditions. The conversion to an equivalent heating value is not applied for
this category.
Sales gas is usually measured and reported in Nm3 (e.g., m3 at 0°C, 760 mm Hg) and
sometimes converted to an energy equivalent [e.g., the volume at normalized gross heating
volume (GHV) of, for example, 9500 kcal/Nm3].
Guidelines for Application of the Petroleum Resources Management System
108
6.7 Summary—Some Guidelines
1. In summing 2P reserves values, arithmetically add the deterministic estimate of volumes.
2. Arithmetic summation of Proved Reserves for independent units leads to a conservative
estimate for the Proved total. Methods and tools are available to determine a more realistic
value (Monte Carlo, probability trees, and customized tools) for summation of independent
distributions.
3. Adding Proved Reserves probabilistically without fully accounting for dependencies could
overstate the Proved total.
4. In calculating reserves volumes from well-performance extrapolation or DCA, always work
up from the lowest aggregation level (e.g., well or string). Adding up Proved Reserves from
well-based DCA estimates may lead to overly conservative estimates of reserves at the
reservoir level of aggregation; hence, always check with an overall reservoir performance
extrapolation. Also, carefully review the “history-to-forecast” interface to make sure that the
methodology has not introduced any discontinuities.
5. PRMS allows probabilistic aggregation up to the field, property, or project level. Typically,
for reporting purposes, further aggregation uses arithmetic summation by category. Fully
probabilistic aggregation of a company’s total reserves and risked Contingent and
Prospective Resources may be used for portfolio analysis.
6. For adding volumes with differing ranges of uncertainty and volumes that are correlated, or
in situations where discount factors are applied, the scenario method can often be applied.
7. When adding volumes, make sure they have a common standard of measurement
(pressure/temperature, calorific value).
References
Carter, P.J. and Morales, E. 1998. Probabilistic Addition of Gas Reserves Within a Major Gas
Project. Paper SPE 50113 presented at the SPE Asia Pacific Oil and Gas Conference and
Exhibition, Perth, Australia, 12–14 October. DOI: 10.2118/50113-MS.
“Determination of Oil and Gas Reserves,” Petroleum Society of the Canadian Inst. of Mining,
Metallurgy, and Petroleum, Calgary, Alberta, Canada (1994).
Harrell, D.R., Hodgin, J.E., and Wagenhofer, T. 2004. Oil and Gas Reserves Estimates:
Recurring Mistakes and Errors. SPE paper 91069 presented at the 2004 SPE Annual
Technical Conference and Exhibition, Houston, 26–29 September. DOI: 10.2118/91069MS.
“Modernization of Oil and Gas Reporting,” US Securities and Exchange Commission (2008).
Petroleum Reserves Definitions. 1997. SPE. http://www.spe.org/industry/reserves/docs/
Petroleum_Reserves_Definitions_1997.pdf.
Petroleum Resources Management System (PRMS). 2007. SPE. http://www.spe.org/spesite/spe/spe/industry/reserves/Petroleum_Resources_Management_System_2007.pdf.
Schuyler, J.R. 1998. Probabilistic Reserves Lead to More Accurate Assessments. Paper SPE
49032 presented at the SPE Annual Technical Conference and Exhibition, New Orleans,
27–30 September. DOI: 10.2118/49032-MS.
Van Elk, J.F., Gupta, R., and Wann, D. 2008. Probabilistic Aggregation of Oil and Gas Field
Resource Estimates and Project Portfolio Analysis. SPERE 13 (1) 82-94. DOI:
10.2118/116395-PA.
Evaluation of Petroleum Reserves and Resources
109
Chapter 7
Evaluation of Petroleum
Reserves and Resources
Yasin Senturk
7.1 Introduction
The valuation process is about determining value. Commercial evaluation of petroleum reserves
and resources is a process by which the value of investing in existing and planned petroleum
recovery projects is determined. These results are used to make internal company investment
decisions regarding commitment of funds for commercial development of petroleum reserves.
Based on a companywide comparative economic analysis of all alternative opportunities
available, the company continues to make rational investment decisions to maximize
shareholders’ value. Results may also be used to support public disclosures subject to regulatory
reporting requirements.
These guidelines are provided to promote consistency in project evaluations and the
presentation of evaluation results while adhering to PRMS (SPE 2007) principles. In this context,
a project evaluation will result in a production schedule and an associated cash flow schedule;
the time integration of these schedules will yield an estimate of marketable quantities (or sales)
and future net revenue [or net present value (NPV) using a range of discount rates, including the
company’s]. The estimation of value is subject to uncertainty due not only to inherent
uncertainties in the petroleum in place and the efficiency of the recovery program but also in the
product prices, the capital and operating costs, and the timing of implementation. Thus, as in the
estimation of marketable quantities, the resulting value estimates should also reflect a range of
outcomes.
Petroleum resources evaluation requires integration of multidisciplinary “know-how” in both
the technical and the commercial areas. Therefore, evaluations should be conducted by
multidisciplinary teams using all relevant information, data, and interpretations.
7.2 Cash-Flow-Based Commercial Evaluations
Investment decisions are based on the company’s view of future commercial conditions that may
impact the development feasibility (commitment to develop) based on production and associated
cash flow schedules of oil and gas projects. Commercial conditions reflect the assumptions made
both for financial conditions (costs, prices, fiscal terms, taxes) and for other factors, such as
marketing, legal, environmental, social and governmental. Meeting the “commercial conditions”
includes satisfying the following criteria defined in PRMS Sec. 2.1.2 for classification as
Reserves:
• A reasonable assessment of the future economics of such production projects meeting
defined investment and operating criteria, such as having a positive NPV at the stipulated
hurdle discount rate.
Guidelines for Application of the Petroleum Resources Management System
110
•
A reasonable expectation that there is a market for all or at least some sales quantities of
production required to justify development.
• Evidence that the necessary production and transportation facilities are available or can be
made available.
• Evidence that legal, contractual, environmental, and other social and economic concerns will
allow for the actual implementation of the recovery project evaluated.
• Evidence to support a reasonable timetable for development.
Where projects do not meet these criteria, similar economic analyses are performed, but the
results are classified under Contingent Resources (discovered but not yet commercial) or
Prospective Resources (not yet discovered but development projects are defined assuming
discovery). Value of petroleum recovery projects can be assessed in several different ways,
including the use of historical costs and comparative market values based on known oil and gas
acquisitions and sales. However, as articulated in PRMS, the guidelines herein apply only to
evaluations based on discounted cash flow (DCF) analysis.
Consistent with the PRMS, the calculation of a project’s NPV shall reflect the following
information and data:
• The production profiles (expected quantities of petroleum production projected over the
identified time periods).
• The estimated costs [capital expenditures (CAPEX) and operating expenditures (OPEX)]
associated with the project to develop, recover, and produce the quantities of petroleum
production at its reference point (SPE 2007 and 2001), including environmental,
abandonment and reclamation costs charged to the project, based on the evaluator’s view of
the costs expected to apply in future periods.
• The estimated revenues from the quantities of production based on the evaluator’s view of
the prices expected to apply to the respective commodities in future periods, including that
portion of the costs and revenues accruing to the entity.
• Future projected petroleum production and revenue-related taxes and royalties expected to be
paid by the entity.
• A project life that is limited to the period of entitlement or reasonable expectation thereof
(see Chapter 10) or to the project economic limit.
• The application of an appropriate discount rate that reasonably reflects the weighted average
cost of capital or the minimum acceptable rate of return (MARR) established and applicable
to the entity at the time of the evaluation.
It is important to restate the following PRMS guidance: “While each organization may define
specific investment criteria, a project is generally considered to be economic if its best estimate
(or 2P) case has a positive net present value under the organization’s standard discount rate.”
7.3 Definitions of Essential Terms
Understanding of essential definitions and well-established industry practices is necessary when
generating and analyzing cash flows for any petroleum recovery project. These include current
and forecast economic conditions, economic limit, and use of appropriate discount rate for the
corporation.
7.3.1 Economic Conditions. Project net cash flow (NCF) profiles can be generated under both
current and future economic conditions as defined in the PRMS. Consistent DCF analyses and
resource evaluations may be conducted using the definitions of economic cases or scenarios:
Evaluation of Petroleum Reserves and Resources
111
Forecast Case (or Base Case): DCF Analysis Using Nominal Dollars. The “forecast
case”(or “base case”) is the standard economic scenario for reserves evaluations. Economic
evaluation underlying the investment decision is based on the entity’s reasonable forecast of
“future economic conditions,” including costs and prices expressed in terms of nominal (or thencurrent) monetary units that are expected to exist during the life of the project. Such forecasts are
based on changes to “current conditions” projected to any year (t). Estimates of any project cash
flow component (price or cost) expressed in terms of base-year or current-year dollars are
escalated (to account for their specific annual inflation rates or escalation rates) to obtain their
equivalent value in terms of nominal dollars (also known as then-current dollars, or dollars of the
day) at any year (t) over its economic life by using the following simple relationship: Nominal $ (t) = (Current-Year $) EFkt = (Current-Year 2010 $) (1+Ek)t
(7.1)
EFkt = (1 + Ek)t
(7.1a)
where
and EFkt is the escalation factor (or the cumulative overall multiplier) at any time t, which ranges
from t = 0 (zero or current-year) to t = n (project’s economic life in years) for any price or cost
component (k = 1, 2, 3…) of project cash flows.
Ek = average and constant annual escalation rate or goods/products and services specific inflation
rate (in fraction) for any price and cost component (k) over the entire project life (t = 0 to n).
Although generally expressed and used as annual rates, these rates can be expressed over any
time period provided that other data are also expressed in the same time unit.
Note that for simplicity alone, periodic escalation rate, Ek, is assumed to remain constant for
any individual price or cost component (k = 1, 2, 3, . .) over the entire project life. (Unless
specified explicitly, the monetary unit is assumed to be US dollars, designated by $).
Constant Case (or Alternative Case. DCF Analysis Using Current-Year Dollars. The
“constant case” is an alternative economic scenario in which current economic conditions are
held constant throughout the project life. PRMS defines current conditions as the average of
those that existed during the previous 12 months, excluding prices defined by contracts or
property specific agreements.
PRMS recommended reserves evaluation under Constant Case requires each price and cost
component of project cash flows to be expressed in terms of current-year dollars. Evaluation
under the Forecast Case uses project cash flows that are expressed in terms of nominal dollars.
Table 7.1 illustrates how an example average crude price of USD 50/bbl in current-year 2010
dollars can be expressed in terms of nominal dollars in Years 2011 through 2012 using Eq. 7.1.
Table 7.1—Oil Price in Different Dollar Units
Crude Price ($/bbl)
Year (t ) Current-Year 2010 $
2010
2011
2012
50.0
50.0
50.0
Nominal $ *
50.00
52.00
54.08
Escalated "Current-Year 2010 $" prices using
an annual price escalation rate of 4%.
Guidelines for Application of the Petroleum Resources Management System
112
For escalation of prices and costs, readers can also refer to SPEE Recommended Evaluation
Practices (2002). However, companies may run several additional economic cases based on
alternative cost and price assumptions to assess the sensitivity of project economics to
uncertainty in forecast conditions.
7.3.2 Economic Limit. The economic limit calculation based on forecast economic conditions
can significantly affect the estimate of petroleum reserves volumes. SPE recommends using
industry standard guidelines for calculating economic limit and associated operating costs
required to sustain the operations. For definitions of revenue, costs and cash flow terms used
here, readers should refer to Sec. 7.4.1.
Economic limit is defined as the production rate beyond which the net operating cash flows
(net revenue minus direct operating costs) from a project are negative, a point in time that
defines the project’s economic life. The project may represent an individual well, lease, or entire
field. Alternatively, it is the production rate at which net revenue from a project equals “out of
pocket” cost to operate that project (the direct costs to maintain the operation) as described in the
next paragraph. For example, in the case of offshore operations, the evaluator should take care to
ensure that the estimated life of any individual reserves entity (as in a well or reservoir) does not
exceed the economic life of a platform in the area capable of ensuring economic production of all
calculated volumes. Therefore, for platforms with satellite tiebacks, the limit of the total
economic grouping should be considered. Scenario or probabilistic modeling can be used to
check the most likely confidence level of making such an assumption.
Operating costs, defined and described in detail in Sec. 7.4.1 and also described in PRMS,
should be based on the same type of projections (or time frame) as used in price forecasting.
Operating costs should include only those costs that are incremental to the project for which the
economic limit is being calculated. In other words, only those cash costs that will actually be
eliminated if project production ceases should be considered in the calculation of economic limit.
Operating costs should include property-specific fixed overhead charges if these are actual
incremental costs attributable to the project and any production and property taxes but (for
purposes of calculating economic limit) should exclude depreciation, abandonment and
reclamation costs, and income tax, as well as any overhead above that required to operate the
subject property (or project) itself. Under PRMS, operating costs may be reduced, and thus
project life extended, by various cost-reduction and revenue enhancement approaches, such as
sharing of production facilities, pooling maintenance contracts, or marketing of associated
nonhydrocarbons. Interim negative project net cash flows may be accommodated in short periods
of low product prices or during temporary major operational problems, provided that the longerterm forecasts still indicate positive cash flows.
7.3.3 Discount Rate. The value of reserves associated with a recovery project is defined as the
cumulative discounted NCF projection over its economic life, which is the project’s NPV.
Project NCFs are discounted at the company’s discount rate (also known as the MARR desired
for and expected from any investment project), which generally reflects the entity’s weighted
average cost of capital (WACC). Different principle-based methods used to determine
company’s appropriate discount rate can be found in Campbell et al (2001) and Higgins (2001).
Finally, it may be useful to restate the following PRMS guidance relevant to the petroleum
resources evaluation process:
• Presentation and reporting of evaluation results within the business entity conducting the
evaluation should not be construed as replacing guidelines for subsequent public disclosure
under guidelines established by external regulatory and government agencies and any current
Evaluation of Petroleum Reserves and Resources
•
•
113
or future associated accounting standards. Consequently, oil and gas reserves evaluations
conducted for internal use may vary from that used for external reporting and disclosures due
to variance between internal business planning assumptions and regulated external reporting
requirements of governing agencies. Therefore, these internal evaluations may be modified to
accommodate criteria imposed by regulatory agencies regarding external disclosures. For
example, criteria may include a specific requirement that, if the recovery were confined to
the technically Proved Reserves estimate, the constant case should still generate a positive
cash flow at the externally stipulated discount rate. External reporting requirements may also
specify alternative guidance on “current economic conditions.”
There may be circumstances where the project meets criteria to be classified as Reserves
using the forecast case but does not meet the external criteria for Proved Reserves. In these
specific circumstances, the entity may record 2P and 3P estimates without separately
recording Proved. As costs are incurred and development proceeds, the low estimate may
eventually satisfy external requirements, and Proved Reserves can then be assigned.
While the PRMS guidelines do not require that project financing be confirmed prior to
classifying projects as Reserves, financing may be another external requirement. In many
cases, loans are conditional upon the project being economic based on Proved Reserves only.
In general, if there is not a reasonable expectation that loans or other forms of financing (e.g.,
farm-outs) can be arranged such that the development will be initiated within a reasonable
time frame, then the project should be classified as Contingent Resources. If financing is
reasonably expected but not yet confirmed, and financing is an external requirement for
reporting in that jurisdiction, the project may be internally classified as Reserves (Justified
for Development), but no Proved Reserves may be reported.
7.4 Development and Analysis of Project Cash Flows
This section describes how project cash flows are developed. Definitions of different cash flow
terms are followed by an overview of its major components (production rates, product prices,
capital and operating costs and other key definitions of ownership interests, royalties, and
international fiscal agreements), including the uncertainties (or accuracy) associated with them
that change over time. The next subsection provides the technical basis and a brief description of
how project DCFs analysis is carried out to establish its value.
7.4.1 Definitions and Development of Project Cash Flows. The cash-flow valuation model
estimates money received (revenue) and deducts all royalty payments, costs (OPEX and
CAPEX), and income taxes, yielding the resulting project NCFs. Detailed definitions, basis, and
description of the key project cash-flow components are provided amply for in Campbell et al.
(2001), Newendorp and Schuyler (2000), and Schuyler (2004). However, even though some
terms may not exist or new terms may appear in different countries, in the basic and simplified
format that works in any country, the project annual NCF at any year t can be expressed in terms
of the following relationship:
NCF(t) = REV(t) - ROY(t) - PTAX(t)-OPEX(t) - OH(t) - CAPEX(t) - ITAX(t) + TCR(t) (7.2)
All affected annual terms above are expressed in applicable working interest (WI) portions are
defined as follows:
NCF(t) = NCF,
REV(t) = revenue = annual production rate (t) times price (t),
ROY(t) = royalty payments = REV(t) times effective royalty rate (t),
Guidelines for Application of the Petroleum Resources Management System
114
PTAX(t) = production tax payments = [REV(t) - ROY(t)] times effective production tax rate (t),
OPEX(t) = OPEX (includes all variable and fixed expenses),
OH(t) = overhead expense (includes all fixed expenses related to management, finance and
accounting and professional fees, etc.),
CAPEX(t) = capital expenditures (tangible and intangible),
ITAX(t) = income tax payments = taxable income (t) times effective income tax rate (t), and
TCR(t) = tax credits received.
Note that the use of word “effective” in the above terms is meant to represent the composite rate
of several applicable factors. For example, production taxes in the US may include severance and
ad valorem taxes, and income tax may include federal and state taxes. It does not mean to
eliminate the need for their inclusion and calculations separately.
To complete the process of generating the project annual net cash flows given by Eq. 7.2, net
revenue, taxable income and income tax payments during any year t are given by the following
definitions:
• Calculation of annual net revenue (NREV):
NREV(t) = REV(t) – ROY(t) - PTAX(t)
•
(7.2a)
Calculation of annual taxable income (TINC):
TINC(t) = NREV(t) - OPEX(t) - OH(t) - EXSI(t) – DD&A(t) – OTAX(t)
(7.2b)
where new annual terms not defined previously are
NREV(t) = net revenue defined by Eq. 7.2a,
TINC(t) = taxable income defined by Eq. 7.2b,
EXSI(t) = expensed investment capital,
DD&A(t) = capital recovery or allowance in terms of depreciation, depletion and amortization
(of allowed nonexpensed investment capital), and
OTAX(t) = other tax payments.
•
Calculation of annual ITAX:
ITAX(t) = TINC (t) • ITR (t)
(7.2c)
where the ITR(t) is the annual effective income tax rate of the corporation.
The revenue and costs components of any term described above (including all other relevant
economic and commercial terms) must be accounted for when deriving project NCF even if they
are defined differently by each entity (e.g., company or government). Definitions of these terms
may differ from country to country due to the fiscal arrangements made between operating
companies and host governments, which allocate the rights to develop and operate specific oil
and gas businesses. Common forms of international fiscal arrangements are concessions (through
royalties and/or taxes) and contracts as described in Chapter 10 and elsewhere (Campbell et al.
2001 and Seba 1998). In general, these agreements define how project costs are recovered and
profit is shared between the host country and the operator. Detailed knowledge of these
governing rules (in royalty, tax, and other incentives) is critical for a credible project reserves
assessment and evaluation process.
Although the generation of these annual project cash-flow components is straightforward, the
accuracy of the estimates (magnitude and quality) is dependent on the property-specific input
Evaluation of Petroleum Reserves and Resources
115
data and forecasting methods used (deterministic or probabilistic) and the expertise of and
effective collaboration among the multidisciplinary valuation team members.
Each component of project NCF terms (such as production rate, product price, CAPEX,
OPEX, inflation rate, taxes, and interest rate) briefly described in Eq. 7.2 has some uncertainty
that changes over time. The terms with significant impact on project NCF are briefly reviewed
below.
Reserves and Production Forecasts. The uncertainty in reserves and associated production
forecasts is usually quantified by using at least three scenarios or cases of low, best and high. For
many projects, these would be the 1P, 2P, and 3P reserves. They could have been generated
deterministically or probabilistically. Many companies, even if the reserves uncertainty is
quantified probabilistically, choose specific reserves cases (as opposed to a Monte Carlo cashflow approach) to run cash flows because this allows a clear link between reserves and
associated development scenarios and costs. In projects with additional Contingent Resources
and exploration upside, companies frequently layer these forecasts on top of the Reserves. This
can lead to overly optimistic evaluations unless the appropriate risks of discovery and
development are applied correctly.
Product Prices. It is important to use the appropriate product prices taking into account the
crude quality or gas heating value. Whatever the method of predicting future oil prices (be it
forward strip or internal company estimates), the differential with a recognized marker crude
(such as West Texas Intermediate or Brent) should be applied. Ideally, it is best to use actual
historical oil price differentials. For new crude blends, a market analyst should review a sample
assay. If the oil is being transported through a pipeline with other crude, the average price for the
blend should be considered, and the evaluator should understand whether a crude banking
arrangement exists or not to allow individual crudes to receive separate price differentials based
on quality (usually API gravity and sulfur content).
For gas, it is important to look at the final sales gas composition after liquids processing to
ensure that the correct differentials are being applied. Each byproduct (e.g., propane, butane, and
condensate) should be evaluated with the appropriate price forecast. Shrinkage of the raw gas
caused by removing liquids and the presence of nonhydrocarbon gases such as CO2 should be
accounted for. Fuel gas requirements should be subtracted from the sales gas reserves.
The transportation costs for both oil and gas should be identified either as part of the
operating costs or as a reduction of the sales price if the sales point is not at the wellhead.
Project Capital Costs. The major components of CAPEX for a typical oil and gas
development project are land acquisition, exploration, drilling and well completion, surface
facilities (gathering infrastructure, process plants, and pipelines), and abandonment.
Drilling and completion well costs are categorized in terms of tangible (subject to
depreciation allowance) and intangible (expensed portion and portion subject to amortization)
well costs.
Surface facility costs are subjected to facility-specific depreciation allowances used in
calculating taxes and various incentives.
Total capital investment cost required for any process equipment (or plant with several units
of equipment) is generally recognized under four categories (Clark and Lorenzoni 1978 and
Humphreys and Katell 1981). Direct costs include all material and labor costs associated with a
purchased physical plant or equipment and its installation. They include the costs of all material
items that are directly incorporated in the plant itself as well as those bulk materials (such as
foundation, piping, instrumentation, etc.) needed to complete the installation. Indirect costs
Guidelines for Application of the Petroleum Resources Management System
116
Conceptual Engineering
Construction
3 to 12 months
1 to 3 years
Evaluation & Planning
Detailed Engineering
3 months to 3 years
1 to 2 years
Start-up
represent the quantities and costs of items that do not become part of, but are necessary costs
involved in, the design and construction of process equipment. Indirect costs are generally
estimated as “percentage of direct costs.” Indirect costs are further subcategorized as
engineering, constructor’s fee (covering administrative overhead and profit), field labor overhead
(FLOH), miscellaneous others and owner’s costs (such as land, organization, and startup costs).
Engineering indirects include the costs for design and drafting, engineering and project
management, procurement, process control, estimating and construction planning. FLOH
includes costs of temporary construction consumables, construction equipment and tools, field
supervision and payroll burden, etc. Miscellaneous others include freight costs, import duties,
taxes, permit costs, royalty costs, insurance and sale of surplus materials. Contingency is
included to allow for possible redesign and modification of equipment, escalated increases in
equipment costs, increases in field labor costs, and delays encountered in startup. Finally,
working capital is needed to meet the daily or weekly cost of labor, maintenance, and purchase,
storage and inventory of field materials.
Equipment sizing and pricing requires a reasonably fixed basic design for budget estimates
and a detailed design for definitive estimates. For equipment sizing and design of oil and gas
handling facilities (in addition to contractor or company-developed standard and analogous
designs), the readers may review a fine reference by Arnold and Stewart (1989, 1991).
There are two fundamental approaches to project cost estimating, the “top-down” and the
“bottom-up.” The top-down approach uses historical data from similar engineering projects to
estimate the costs for the current project by revising and normalizing these data for changes in
time (inflation or deflation), production size, or plant capacity and location and other factors
(such as activity level, weight, and energy consumption). It uses a simple “percentage-of-cost
basis” established from the review of historical or current data. The bottom-up approach is a
more detailed method of cost estimating and requires a detailed design that breaks down the
process plant equipment into small, discrete, and manageable parts (or units). The smaller unit
costs are added together (including other associated costs) to obtain the overall cost estimate for
the process equipment and the plant.
As illustrated by Fig.7.1, a typical project development life (for surface facilities, plants, or
pipelines) encompasses the four phases of initial planning and evaluation, designing and
engineering (conceptual and detailed), construction, and startup, which could take several years
to complete. It represents a series of steps leading to decision points (or gateways) at the end of
each phase where cost estimates are made to determine whether it is economically viable to
proceed to the next step or project phase.
Time (Project Development Stages)
Fig. 7.1—Typical project phases [adapted from Clark and Lorenzoni (1978)].
Although they may be known or defined by different names, the American Association of
Cost Engineers (Humphreys and Katell 1981) recommends three basic categories of project cost
Evaluation of Petroleum Reserves and Resources
117
estimates according to detail and accuracy required by their intended use (during project phases
illustrated in Fig. 7.1), which are approximately defined as follows:
• Order of magnitude estimate is considered accurate within - 30% to + 50%. Based on costcapacity curves and ratios, this cost estimate is made during the initial planning and
evaluation stage of a project, and used for investment screening purposes.
• Preliminary estimate is considered accurate within - 15 to + 30%. Based on flow sheets,
layouts, and equipment details, the semidetailed cost estimate is made during the conceptualdesign stage of a project, and is used for budget proposal and expenditure approval purposes.
• Definitive estimate is considered accurate within - 5 to + 15%. Based on detailed and welldefined design and engineering data (with complete sets of specifications, drawings,
equipment data sheets, etc.), this estimate is made during the detailed engineering and
construction stage of a project and is used for procurement and construction.
Project Operating Costs. Similar to capital costs, estimation and treatment of OPEX in
various categories could also be important for the purpose of calculating tax and project
profitability. Estimates of OPEX in base-year, or current-year, dollars are generally based on an
analogous operations, adjusted for the production capacity, manpower, and appropriate costescalation (or cost-component specific inflation) rates. Operating cost estimates are generally
performed on a unit-of-production, monthly, or annual basis.
OPEX are generally recognized under five categories (Humphreys and Katell 1981). Direct
costs are considered to be dependent on production and include variable and semivariable
components. At production shutdowns (with zero production or throughput), direct costs are
generally represented at a reasonable minimum basis of about 20% or greater of the semivariable
costs estimated for an operation at full capacity. Indirect costs are considered independent of
production and include plant overhead, or burden, and fixed costs such as property taxes,
insurance and depreciation. General and administration expenses (G&A), or simply overhead
expenses, are those costs incurred above the factory or production level and are associated with
home office or headquarters management. This category includes salaries and expenses of
company officers and staff, central engineering, research and development, marketing and sales
costs, etc. Distribution costs are those operating and manufacturing costs associated with
shipping the products to market, like pipelines for crude oil, gas sales, and natural gas liquids.
They include the cost of containers and packages, freight, operation of pipelines, terminals, and
warehouses or storage tanks. Contingencies constitute an allowance made in an operating cost
estimate for unexpected costs or for error or variation likely to occur in the estimate. A
contingency allowance is just as important in the OPEX as it is in the CAPEX. However, it must
be pointed out that companies may define and categorize their operating costs differently and
may not even include some of the components in their project economic analysis.
Other Key Terms and Definitions. Ownership Interest represents the share, right, or title in
property (a lease, concession, or license), project, asset, or entity. The most commonly known
type of ownership (or economic) interests are: WI, net WI, mineral interest, carried interest,
back-in interest, and reversionary interest.
Royalties are the payments made to the landowner or the mineral interest owner for the right
to explore and produce petroleum after a discovery. They are made to the host government or
mineral owner (lessor) in return for depletion of the reservoirs and granting the producer
(lessee/contractor) access to the petroleum resources. Many agreements allow for the producer to
lift the royalty volumes, sell them on behalf of the royalty owner, and pay the proceeds to the
Guidelines for Application of the Petroleum Resources Management System
118
owner. Some agreements provide for the royalty to be taken only in kind (e.g., in terms of
production) by the royalty owner.
Royalty Interest is a mineral interest that is not burdened with a proportionate share in
investment and operating costs. Royalty owners are responsible for their share of production and
ad valorem taxes (i.e., taxes imposed based on production value and/or value of equipment
necessary to produce petroleum). Royalty interest may also be defined as the share of minerals
reserved in money, or in kind, free of expense, by the owner of mineral interest or a fee received
when leasing the property to another party for exploration and production.
Overriding royalty interest is a fraction of wellhead production owned free of any cost
obligation. It is an economic interest created in addition to the royalty stated in the basic lease.
International Fiscal Arrangements made between the producer and the host government may
include concession agreements, joint venture agreements and contracts (production sharing and
service [refer to Chap. 10, PRMS (SPE 2007), Campbell et al. (2001), and Seba (1998)].
7.4.2 Analyzing Project Cash Flows and Establishing Value. The generally accepted figure of
merit or value for any petroleum recovery project is defined by cumulative discounted NCF or
the NPV generated over its economic (or contractual) life cycle illustrated by Fig. 7.2.
NCF3
NCF1
0
NCFn-1
NCFn
+
-
0
1
2
(n-1)
3
n
Time (t)
NCF0= IC
Fig. 7.2—A typical project net cash flow diagram.
The value of any project can be expressed mathematically by the following DCF-based
valuation model or the NPV equation:
t
NPV( , MARR ) =
n
∑
n
NCFt
= ∑ NCFt • DFt
t
t = 0 (1 + MARR )
t =0
(7.3)
NPV ( , MARR ) = N CF0 + NCF1 • DF1 + NCF
2
t
and can also be rewritten in the following open form:
• DF2 + .. + NCF n • DFn
(7.3a)
where
NCFt = annual year-end NCF (revenue minus cost) at any year (t) ranging from 0 to n and
NCF0 = the initial investment capital (IC) made as a single lump sum in the first or “0” year-end
for the most projects. However, for large projects, the initial CAPEX profile does span more than
one year and thus, the NCFt’s for (t) ranging from initial (0) to say (m) years would be negative
during these early years. They are actually spent as nominal dollars during these earlier m years
and are also equivalent to their future value (FVI) assumed to be spent only in zero-year (or
current-year) as a lump-sum initial investment capital (IC or NCF0) and can now be defined as
follows:
Evaluation of Petroleum Reserves and Resources
t
IC = NCF0 = FVI ( , MARR ) =
m
∑
t =0
m
IC t (1 + MARR ) t = ∑ IC t / DFt
119
(7.3b)
t =0
This manipulation is necessary not to discount future project cash flows for another m years and
thus provide the same comparative basis for all projects included in a company’s investment
portfolio. As a result, each project will show the positive cash flow in the actual year where
revenue begins, and this ensures consistent discounting of future cash flows among all competing
investment projects. Variables in Eqs. 7.3 through 7.3b are defined as follows:
MARR = Minimum attractive rate of return desired or the company’s annual discount rate,
t = time starting from zero (0) or current-year to (n) years in the future,
n = project economic (or contractual) life in years,
m = number of years (usually 2 to 5 for megaprojects) during which initial project capital is
actually spent,
DFt = discount factor at any year (t) defined as follows:
DFt = 1/[1+MARR]t for the year-end cash receipts
(7.3c)
DFt = 1/[1+MARR](t-0.5) for the mid-year cash receipts
(7.3d)
Eqs. 7.3 through 7.3c assume project annual NCFs are received only at year-end. However, if
they are received at mid-year then the appropriate discount factor (DFt) defined by Eq. 7.3d must
be used. For discounted cash-flow analysis, readers can also refer to SPEE (2002).
According to PRMS guidelines, a discovered petroleum development project is considered
commercial and its recoverable quantities are classified as Reserves when its evaluation has
established a positive NPV and there are no unresolved contingencies to prevent its timely
development. If the project NPV is negative and/or there are unresolved contingencies
preventing the project implementation within a reasonable time frame, then technically
recoverable quantities must be classified as Contingent Resources.
Finally, in addition to project NPV described above, there are other important measures of
profitability [such as the internal rate of return, profitability index (dollar generated per dollar
initially invested), payout time, or payback period] that are routinely used in project economic
evaluations (Campbell et al. 2001, Higgins 2001, Newendorp and Schuyler 2000, Seba 1998, and
COGEH 2007).
7.5 Application Example
A relatively small but prolific international oil field (with its associated gas) is jointly owned by
several independent North American producers. The company in this example evaluation has a
one-third WI ownership in the property.
The PRMS guidance on evaluations states that: “While each organization may define specific
investment criteria, a project is generally considered to be ‘economic’ if its ‘best estimate’ (2P or
P50 in probabilistic analysis) case has a positive NPV under the organization’s standard discount
rate. It is the most realistic assessment of recoverable quantities if only a single result were
reported.” Therefore, it is judged to be prudent and useful to generate the results of economic
evaluation reserves for this example petroleum-development project using production profiles
based on the low estimate (Proved, or 1P), the best estimate (Proved plus Probable, or 2P), and
the high estimate (Proved plus Probable plus Possible, or 3P) of oil reserves. Moreover, similar
to reserves assessment using probabilistic approach in Chapter 5, an economic evaluation of
Guidelines for Application of the Petroleum Resources Management System
120
these three scenarios may also be carried out using stochastic (probabilistic) decision analysis,
which is briefly described at the end of this chapter, including its application to the PRMS
Forecast Case economic evaluation of the example oil project.
7.5.1 Basic Data and Assumptions. The example petroleum recovery project is developed at an
initial annual depletion rate of about 11% of the respective estimated ultimate recovery (EUR)
values of 1P, 2P, or 3P Reserves. The project has been producing under an effective pressure
maintenance scheme supported by downdip water injection. Fig. 7.3 presents oil production
profiles based on the low (1P), best (2P), and high (3P) estimates of oil reserves (i.e., the
company’s WI share only).
22,000
3P = 71.6
20,000
70
18,000
60
Oil Rate (Qt), STB/D
16,000
14,000
2P = 48.5
12,000
50
40
10,000
1P = 32.4
8,000
30
6,000
20
4,000
Cumulative Production (MMSTB)
80
10
2,000
0
0
0
2
4
6
8
10
12
14
16
18
20
22
24
Time (t), years
Fig. 7.3—Example Evaluation: Production rate profiles and reserves.
It is important to emphasize that production profiles are independently developed based on
different oil initially in-place (OIIP) estimates and hence the reserves categories represent the
low, best, and high scenarios. Table 7.2 summarizes key parameters defining current and future
economic conditions.
Table 7.2—Example Evaluation: Key Economic Parameters
Estimate
Current Economic Conditions:
Current-year 2010 Oil Price ($/bbl)
Current-year 2010 Gas Price ($/MMBtu)
60
5
Future Economic Conditions
(beyond the current-year 2010 and over the project life):
Average Annual Product Price & Cost Escalation Rates (%)
Oil Prices
Gas Prices
Operating Expenditures (OPEX)
Capital Expenditures (CAPEX)
3%
3%
3.5%
4%
Average Annual Inflation Rate (f)
Average Nominal Discount Rate (ANDR )
3%
10%
Evaluation of Petroleum Reserves and Resources
121
Furthermore, Table 7.3 summarizes the cost estimates and other relevant company-specific
data assumed and necessary to carry out the example oil project evaluation for all three reserves
scenarios.
Key economic assumptions and project cost estimates (Tables 7.2 and 7.3) are considered
reasonable. Although the quality of input data is very important for assessment of reserves
volumes and project value, it does not impact the methodology of the evaluation process
described here.
Table 7.3—Example Evaluation: Basic Reserves and Cost Data
Type of Basic Data Required
The Low Estimate
(1P)
The Best Estimate
(2P)
The High Estimate
(3P)
Oil Reserves (MMSTB)
Solution GOR (scf/STB)
Solution Gas Reserves (Bscf)
Gross Heating Value of Gas (Btu/scf)
Initial Oil Rate (MSTB/D)
Initial Investment Capital, IC (MM$)
32.4
600
19.4
1,330
10
140
48.5
600
29.1
1,330
15
180
71.6
600
42.9
1,330
20
230
8
8
10
12
12
18
20%
10%
25% per year
35%
20%
10%
25% per year
35%
20%
10%
25% per year
35%
Annual Future Expenses and Capital (2010 MM$)
- OPEX
- CAPEX (only in 5th/10th/15th years)
Effective Royalty Rate
Effective Production Tax Rate
Declining Balance Depreciation Rate
Effective Income Tax Rate
Finally, based on the project basic economic data summarized in Tables 7.2 and 7.3, the
projected oil and gas production rates, and forecasts of product prices and costs, the cash flow
development process (described in Sec. 7.4) is used to generate the relevant project NCF
projections over its 25-year economic life for the following two PRMS economic scenarios:
• Forecast Case (Base Case) Economic Scenario: All project cash flows are expressed in
terms of nominal dollars calculated by escalating the project cash flows in terms of currentyear 2010 dollars using the appropriate annual price and cost escalation and inflation rates in
Table 7.2.
• Constant Case (Alternative Case) Economic Scenario: Project cash flows are expressed in
terms of current-year 2010 dollars, and all future annual price and cost escalation and
inflation rates are assumed to be zero during the entire project life of 25 years.
It is a good practice to test for the economic limit as a project approaches the end of its
productive life. In this example, the net cash flows for the three profiles remain positive at the
end of the 25 year project period.
7.5.2 Summary of Results. Due to its relatively small size and the availability of analog projects
completed in the same producing area, the project is expected to be completed by a reputable
contractor in less than 18 months from its approval. It is further assumed that contract drilling
rigs and the off-the-shelf design details on the required gas/oil separator, water injection plants,
and related pipelines are readily available. Fig. 7.4 illustrates the example project’s CAPEX
profiles for the initial investment spent in terms of 2010 dollars during 2 years for these three
reserves scenarios evaluated.
Guidelines for Application of the Petroleum Resources Management System
122
Capital (2010 MM$)
140
P1
120
P2
P3
100
80
60
40
20
0
2009
2010
Fig. 7.4—Evaluation Example: Expenditure profiles of initial capital investment.
The value of the example petroleum project owned by an independent producer (with a onethird WI) is evaluated using its appropriate annual discount rate assumed to be at 10%/yr.
Based on development of three plausible reserves estimates and associated production
profiles presented in Fig. 7.3, discounted annual and cumulative NCF profiles under PRMS
Forecast Case and Constant Case assumptions can be generated for each reserves scenario. Fig.
7.5 illustrates these profiles only for the 2P reserves scenario.
200
800
$740
$623
100
400
Development Base: The Best Estimate (2P)
50
0
0
2
4
6
8
10
-50
-100
-150
600
12 14 16
Time (t), years
18
20
22
Annual
Cumulative
740
24
0
-200
NCF (MM$) Forecast Case Constant Case
NPV @ 10%
200
623
-200
-400
Cumulative NCF (MM$)
Annual NCF (MM$)
150
-600
-800
Fig. 7.5—Evaluation example using the best reserves estimate (2P):
Discounted Net Cash Flow (NCF) projections (million $) at 10%.
Table 7.4 provides a comparative summary of results based on 1P, 2P, and 3P reserves
scenarios and associated project profitability measures estimated under both economic cases.
Evaluation of Petroleum Reserves and Resources
123
Table 7.4—Evaluation Example: Basis and Estimated Project Profitability Measures
Key Parameters (in 2010 $’s)
The Low Estimate
(1P)
The Best Estimate
(2P)
The High Estimate
(3P)
32.4
19.4
10
140
48.5
29.1
15
180
71.6
42.9
20
230
467
392
740
623
1,139
958
81%
76%
96%
90%
107%
101%
5.1
4.5
6.0
5.2
Oil Reserves (MMSTB)
Associated Gas Reserves (Bscf)
Initial Oil Rate (MSTB/D)
Initial Investment Capital, IC (MM$)
Value of Petroleum Reserves or
Net Present Value, NPV @ 10%
Forecast Case
Constant Case
DCF Rate of Return, DCF-ROR (%):
Forecast Case
Constant Case
Profitability Index ($ Returned per $ Initially Invested):
Forecast Case
Constant Case
4.3
3.8
As summarized in Table 7.4, the project’s NPV profit (or value of its petroleum reserves)
estimated using the Forecast Case (with higher project NCFs in nominal dollars) is determined to
be greater than that obtained using the Constant Case (with lower project NCFs expressed in
current-year 2010 dollars) when both project NCFs are discounted at the same company annual
nominal discount rate of 10%.
Under the price and cost estimates (including their future projections) and assumptions used,
the example petroleum project is determined to be a very attractive investment opportunity for
the corporation with an estimated annual DCF rate of return exceeding 75% for all economic
scenarios studied, providing a substantial margin of safety (or degree of certainty) over the
desired annual MARR of 10%. However, whether this particular project is finally included in the
company’s current investment portfolio or not will strictly depend on both the relative economic
merits of other competing investment opportunities and the amount of investment capital
available.
Finally, Fig. 7.6 shows the results of a sensitivity analysis in a typical tornado diagram form:
Oil Price
Discount Rate
CAPEX
30% Increase
30% Decrease
OPEX
-60% -50% -40% -30% -20% -10%
0%
10% 20% 30% 40%
Changes in Project NPV (%)
Fig. 7.6—Results of sensitivity analysis.
Guidelines for Application of the Petroleum Resources Management System
124
The tornado diagram illustrates the impact on project NPV (based on 2P scenario) of predefined
constant ± 30% (positive and negative percent) changes in major cash-flow components,
including the discount rate. Similar charts also could be constructed to illustrate the sensitivity of
other project profitability measures, such as rate of return, profitability index, and payout time,
etc. Sensitivity analysis clearly demonstrates that project NPV is more sensitive to revenue (oil
price and similarly to production rate) than it is to costs, especially the operating costs. A
constant ± 30% change in the selected major parameters would change this example project NPV
(also approximately valid for the development of any reserves or resources category) as follows:
• Oil price (and production rate) would change it by ± 37%, with a direct relationship.
• Other parameters impact the NPV inversely, as expected [e.g., (+) changes resulting in (–)
changes in NPV and vice versa]. It follows that
- Discount rate would change it by -17% and +22%, respectively,
- CAPEX would change it by -5% and +5%, respectively, and
- OPEX would change it by -2% and +2%, respectively.
However, although impact of capital, and especially the operating expenditures, on project
economics appears to be relatively minor, the need for consistency and accuracy in their
estimates cannot be overemphasized as they are routinely used to estimate company’s unit
annual development and operating costs (in $/bbl) both on a project and a companywide basis.
7.5.3 Decision Analysis Based on Expected Value (EV) Concept (Campbell et al. 2001,
Newendorp and Schuyler 2000, Schuyler 2004). Decision analysis is a structured process
based on a clear objective(s) and criteria that are used to evaluate, compare, and make rational
decisions on many definable problems, including investment projects.
In deterministic analysis, investment decisions are generally made by evaluating and
comparing the project NPVs in a portfolio of projects competing for capital funds. In the
Forecast Cases of the example recovery project, NPV was deterministically estimated to be about
USD 467 million, USD 740 million and USD 1,139 million, respectively, for the 1P, 2P, and 3P
estimates of petroleum reserves.
In stochastic analysis, on the other hand, the EV concept is used to probabilistically estimate
project profitability measures. EV is the probability-weighted value of all possible outcomes,
which is the sum of all outcome values Xi times their respective probabilities of occurrence p(xi)
[where subscript (i) could range from 1 to n], and can be mathematically expressed by
EV = ∑Xi • p (xi)
(7.4)
where the summation is taken over (n) outcomes irrespective of whether the outcomes represent
different categories of petroleum resources, monetary values, DCF rates of return or any other
values of a random occurrence.
Two most common methods used to stochastically assess petroleum resources and/or
evaluate project economics are briefly described below.
Decision Tree Analysis (DTA). Using Eq. 7.4 at each successive node, DTA can be used to
derive the expected monetary value (EMV) of the project at any discount rate (or MARR), which
now replaces the project NPV deterministically determined earlier (see Eqs. 7.3), as follows:
EMV@ MARR = ∑EMVi • p (xi)
where EMVi represent the EMV for ith outcome, etc.
(7.5)
Evaluation of Petroleum Reserves and Resources
125
In the simplest possible application of DTA and for illustration purpose only, let us assume
that the deterministically estimated incremental project reserves with varying degrees of
uncertainty and their associated NPVs have average probabilities of occurrence of 97% (for
Proved), 70% (for Probable instead of being ≥ 50% as a range for 2P, etc), and 30% (for
Possible). They represent generalized approximations, or “weighting factors,” that are valid for
the majority of cases using a log-normal “cumulative probability distribution curve,” which is
also known as an “expectation curve” (EC). The expected (or mean) value for any random
variable is equivalent to and defined by the area under its specific EC. Therefore, using Eqs. 7.4
and 7.5, the expected reserves value (ERV) and the EMV for the example petroleum project can
be calculated as follows:
ERV = (0.97) x32.2 + (0.7) x (48.5-32.2) + (0.3) x (71.6-48.5) = 50.1 MMSTB
EMV at 10% = (0.97) x 467 + (0.7) x (740-467) + (0.3) x (1,139-740) = USD 763 million
These expected values would approach their best estimates or 2P values (of 48.5 MMSTB
and USD 740 million for the Forecast Case) if their expectation curves were normally
distributed.
Monte Carlo Simulation (MCS) Technique. It uses a simple sampling technique that amounts
to integrating Eq. 7.4. It is based on the DCF model defined by Eq. 7.3. and specific probability
distribution curves similar to those presented in Fig. 7.7, which are defined for each key random
variable with significant ranges of uncertainty.
In a simplified cash-flow model, project NCF at any time (t), defined earlier by Eq. 7.2 and
required by Eqs. 7.3 through 7.3b, may be expressed in terms of these key probabilistic (or
random) variables as
NCFt = [Volume(t) – Royalty(t)] (t) x Price (t) - CAPEX (t) – OPEX(t) – Taxes (t)
(7.6)
Uncertainty around each random variable in Eq.7.6 may be represented by one of the following
common probability-density functions (or probability distribution curves) presented in Fig.7.7.
The selection of a distribution curve appropriate for any random variable should be based on the
judgments of the subject-matter experts.
Most likely (Mode)
Probability
Probability
Min
Max
Max
Min
Normal
Mean
Probability
Mode
Probability
Mean & Mode
Triangular
Lognormal
Uniform
Fig. 7.7—Common probability distribution curves.
Selecting and using the probability distribution curve [or probability-density function (PDF)]
appropriate for each random variable and accounting for other fixed input parameters in the cashflow model (see Eqs. 7.3 and 7.6), MCS sampling technique randomly generates the estimates of
project annual NCFs over the study period and the resulting single EMV at each trial. After
hundreds or thousands of trials, it can generate the project NCF profiles representing different
confidence bands, associated EMVs, and hence the resulting EMV profile (or profiles for other
profitability measures as well). Results are usually presented in terms of both PDFs
Guidelines for Application of the Petroleum Resources Management System
126
(approximately bell-shaped distribution curves) and ECs, as illustrated for the EMV profiles of
the example evaluation project on the right side of Fig. 7.8.
Based on the assumptions made and input data (given in terms of probability distribution
curves and as fixed parameters illustrated in the left side of Fig.7.8) used for the example
petroleum project, the data for the simulated EMV profiles are generated by using the MCS
technique and plotted in the right side of Fig. 7.8. As a result, the stochastically established P90,
P50, and P10 values of the project EMVs (discounted at 10%) for the Forecast Case are
estimated to be about USD 500, USD 705, and USD 995 million, respectively. They compare
with the deterministic NPVs (also discounted at 10%) of about USD 467 million (1P), USD 740
million (2P), and USD 1,139 million (3P), respectively. Moreover, the mean monetary value of
the project (EMV at 10%), is equivalent to the area under either of its EMV profiles shown on
the right side of Fig. 7.8 and is estimated to be USD 846 million as compared with USD 763
million estimated using DTA (or EV analysis) applied to deterministic estimates. It must be
noted that only the mean values of probabilistic estimates (Reserves or associated EMVs) may be
added together among projects (refer to Chapter 6 for more details).
It is important to point out that MCS technique provides the evaluator with a significant
advantage over the deterministic analysis using the scenario approach and especially over
traditional sensitivity analysis. MCS provides not only the project’s expected profitability
measures like EMV, expected DCF rate of return, and expected profitability index etc., but also
their profiles over a wide range of uncertainties quantified in terms of PDFs and ECs similar to
the ones presented for the example project’s EMV on the right side of Fig. 7.8.
Key Probalistic Variables
50%
100%
Outputs
A
Cash Flow Model
NPV @ 10% =
n
B
NCFt
∑ (1 + MARR )
t =0
t
C
80%
40%
60%
30%
40%
20%
20%
10%
Probability
Inputs
Cumulative Probability ≥
Expectation Curve
MARR = 8% (Discount Rate)
A = Projections of Prices, Net Cash Flow, etc.
B = EMV Profile (Expectation Curve)
Fixed Parameters
C = Expectation Profiles for DCF -ROR,
Profitability Index, etc.
500
0%
-400
0
400
995
705
800
0%
1200
1600
Expected Monetary Value, EMV @ 10% (MM$)
Fig. 7.8—Example evaluation: Project’s EMV profiles generated by the MSC technique.
References
Arnold, K. and Stewart, M. 1991. Surface Production Operations, Design of Oil-Handling
Systems and Facilities, 1. Houston, Texas: Gulf Publishing Company.
Arnold, K. and Stewart, M. 1989. Surface Production Operations, Design of Gas-Handling
Systems and Facilities, 2. Houston, Texas: Gulf Publishing Company.
Campbell, J.M. et al. 2001. Analyzing and Managing Risky Investments. Norman, Oklahoma:
John M. Campbell.
Canadian Oil and Gas Evaluation Handbook (COGEH). 2007. Calgary, Alberta: Society of
Petroleum Evaluation Engineers 1.
Evaluation of Petroleum Reserves and Resources
127
Clark, F.D. and Lorenzoni, A.B. 1978. Applied Cost Engineering. New York: Marcel Dekker.
Guidelines for the Evaluation of Petroleum Reserves and Resources. 2001. SPE.
http://www.spe.org/industry/docs/GuidelinesEvaluationReservesResources_2001.pdf.
Higgins, R.C. 2001. Analysis for Financial Management. 2001. New York: Irwin McGraw-Hill.
Humphreys, K.K. and Katell, S. 1981. Basic Cost Engineering. New York: Marcel Dekker.
Newendorp, P.D. and Schuyler, J.R. 2000. Decision Analysis for Petroleum Exploration, second
edition. Aurora, Colorado: Planning Press.
Petroleum Resources Management System (PRMS). 2007. SPE. http://www.spe.org/industry/
docs/Petroleum_Resources_Management_System_2007.pdf.
Schuyler, J.R. 2004. Decision Analysis Collection. Aurora, Colorado: Planning Press.
Seba, R.D. 1998. Economics of Worldwide Petroleum Production. Tulsa, Oklahoma: OGCI
Publications.
SPEE Recommended Evaluation Practice #7—Escalation of Prices and Costs. 2002a. Houston,
Texas: Society of Petroleum Evaluation Engineers.
SPEE Recommended Evaluation Practice #5—Discounting Cash Flows. 2002b. Houston, Texas:
Society of Petroleum Evaluation Engineers.
Guidelines for Application of the Petroleum Resources Management System
128
Chapter 8
Unconventional Resources
Estimation
8.1 Introduction
Phil Chan
Two types of petroleum resources have been defined that may require different approaches for
their evaluations:
• Conventional resources exist in discrete petroleum accumulations related to a localized
geological structural feature and/or stratigraphic condition (typically with each accumulation
bounded by a down-dip contact with an aquifer) that is significantly affected by
hydrodynamic influences such as the buoyancy of petroleum in water. The petroleum is
recovered through wellbores and typically requires minimal processing prior to sale.
• Unconventional resources exist in hydrocarbon accumulations that are pervasive throughout
a large area and that are generally not significantly affected by hydrodynamic influences
(also called “continuous-type deposits”). Such accumulations require specialized extraction
technology, and the raw production may require significant processing prior to sale.
Conventional
Reservoirs
Conventional
Heavy
Oil
Extra –Heavy Oil
Tight Gas
Formations
Increased Pricing
Bitumen
Improved Technology
Basin-centered Gas
Unconventional
Coal Bed Methane
Shale gas
Oil Shale
Gas Hydrates
(modified from Holditch, JPT Nov. 2002)
Fig. 8.1—Resource triangle.
The relationship of conventional to unconventional resources is illustrated by a resource
triangle (Fig. 8.1). Heavy oil and tight gas formations straddle the boundary; nonetheless, both
present challenges in applying the assessment methods typically used for conventional
accumulations.
Very large volumes of petroleum exist in unconventional reservoirs, but their commercial
recovery often requires a combination of improved technology and higher product prices.
Industry analysts project that unconventional liquids reservoirs (excluding oil shale) may contain
4.8 trillion bbl initially-in-place. Oil shales may add another 1 to 3 trillion bbl. The in-place
Unconventional Resources Estimation
129
estimates for unconventional gas accumulations range up to 30,000 Tscf (excluding gas
hydrates) vs. 2,800 Tscf produced to date. Estimates for gas hydrates vary widely between
60,000 and 700,000 Tscf; however, no commercial recovery methods have yet been developed to
extract these in-place volumes.
8.1.1 Assessment and Classification Issues. The Petroleum Resources Management System
(PRMS) resources definitions, together with the classification system, are intended to be
appropriate for all types of petroleum accumulations regardless of their in-place characteristics,
the extraction method applied, or the degree of processing required. However, specialized
techniques often are employed in assessing in-place quantities and evaluating development and
production programs of unconventional resources.
Estimations of recoverable resource quantities must include an estimate of the associated
uncertainty expressed by allocation to PRMS categories using the same low/best/high
methodology as for conventional resources. Typically, the assessment process begins with
estimates of original-in-place volumes. Thereafter, portions of the in-place quantities that may be
potentially recovered by identified development techniques are defined. In some cases, there are
no known technical methods of recovery and the in-place volumes are classified as
Unrecoverable.
As in conventional accumulations, undiscovered recoverable volumes are classed as
Prospective Resources and are estimated contingent on their discovery and commercial
development. PRMS recognizes that the hydrocarbon type and/or the reservoir may not support a
flowing well test but the accumulation may be classed as Discovered based on other evidence
(e.g., sampling and/or logging).
It is not uncommon to recognize very large areas where prior drilling results have identified
the presence of a Discovered resource type that, based on analogs, has production potential.
Where technically feasible, recovery techniques are identified, but when economic and/or other
commercial criteria are not satisfied (even under very aggressive forecasts), estimates of
recoverable quantities are classified as Contingent Resources and subclassified as Development
Not Viable. If the recovery processes have been confirmed as not technically feasible, the inplace volumes are classified as Discovered/Unrecoverable. As the play and technologies mature
and development projects are better defined, portions of estimated volumes may be assigned to
the Contingent Resources subclasses that recognize this progressive technical and commercial
maturity. Typically, Reserves are only attributed after pilot programs have confirmed the
technical and economic producibility and after capital is allocated for development.
In many cases, the raw production must be further processed to yield a marketable product.
Integrated development/processing projects include the cost of the processing and related
facilities in the project economics. In other cases, the raw production is sold to a third party (at a
reduced price) for further processing. In either case, development economics are highly
dependent on the capital and operating costs associated with complex processing facilities.
As a result of their recent emergence as commercial ventures, the publicly available literature on
the standard assessment methods and the illustrative examples for unconventional resources is
limited. In addition, these accumulations are often pervasive throughout a very large area and are
developed with high-density drilling; probabilistic assessment techniques may be more
applicable than in conventional plays. While the authors have quoted some “rules of thumb” on
drainage areas and drilling spacing unit offsets related to reserves categorization, it must be
recognized that our overall goal is to assign appropriate confidence to commercial producibility;
this relationship may be much more complex than in conventional reservoirs.
Guidelines for Application of the Petroleum Resources Management System
130
The following sections by different authors provide an overview of each resource type and
preliminary information on evaluation approaches. It is envisioned that these sections will be
updated and expanded in future editions.
8.2 Extra-Heavy Oil
8.3 Bitumen
8.4 Tight Gas Formations
8.5 Coalbed Methane
8.6 Shale Gas
8.7 Oil Shale
8.8 Gas Hydrates
8.2 Extra-Heavy Oil
John Etherington
8.2.1 Introduction. Crude oil may be divided into categories based on density and viscosity.
Heavy crude oil is generally defined as having a density in the range of 10 to 23° API with a
viscosity that is typically less than 1,000 cp. Although heavy crude oil is often recovered in
thermal EOR projects, it is typically not a continuous accumulation and often does not require
upgrading. Therefore, heavy crude is defined herein as Conventional Resources regarding
assessment methods and classification under PRMS guidelines. Extra-heavy oil density is less
than 10° API with a viscosity ranging from 1,000 to 10,000 cp. While mobility is limited,
accumulations typically have defined oil/water contacts and exhibit normal buoyancy effects.
Extra-heavy oil is herein classified as unconventional resources because it typically requires
upgrading.
About 90% of the world’s known accumulations of extra-heavy oil are in the Orinoco Oil
belt of the Eastern Venezuelan basin, with over 1.3 trillion bbl initially-in-place (Dusseault
2008). Depending on technology developments and associated economics, ultimate recoverable
volumes are estimated at 235 billion barrels (Dusseault 2008).
8.2.2 Reservoir Characteristics—Risk and Uncertainty. Individual sand bodies in the Orinoco
accumulations range in thickness up to 150 ft. The majority of oil-bearing beds are 25 to 40 ft
thick, with high porosity (27 to 32%), good permeability (up to 5 darcies), and good lateral
continuity (Dusseault 2001). The major uncertainties are fault compartmentalization and water
encroachment.
In the Orinoco Oil belt, cold production of extra-heavy oil is normally achieved through
multilateral (horizontal) wells that are positioned in thin but relatively continuous sands, in
combination with electric submersible pumps and progressing cavity pumps. Horizontal
multilateral wells maximize the borehole contact with the reservoir. Extra-heavy oil mobility in
the Orinoco Oil Belt reservoirs is typically greater than that of bitumen in the Alberta sands
because of higher reservoir temperatures, greater reservoir permeability, higher gas/oil ratio, and
the lower viscosity of extra-heavy oil. The recovery factor for an extra-heavy oil cold-production
project in the Orinoco Oil belt is estimated to be approximately 12% of the in-place oil. While
upside secondary recovery with thermal projects is forecast, these incremental volumes would be
classed under PRMS as Contingent Resources until pilots are complete and thermal projects are
sanctioned.
Unconventional Resources Estimation
131
The majority of Orinoco production is diluted and transported to the Caribbean coast for
upgrading prior to sale; thus, economics must incorporate upgrading costs either as integrated
projects or through reduced pricing at the field-level custody-transfer point.
References
Dusseault, M.B. 2001. Comparing Venezuelan and Canadian Heavy Oil and Tar Sands. Paper
2001-061 presented at the Petroleum Society’s Canadian International Petroleum
Conference, Calgary, 12–14 June.
Dusseault, M.B., Zambrano, A., Barrios, J.R., and Guerra, C. 2008. Estimating Technically
Recoverable Reserves in the Faja Petrolifera del Orinoco: FPO. Paper WHOC08 2008-437,
World Heavy Oil Congress.
8.3 Bitumen
John Etherington
8.3.1 Introduction. Natural bitumen is the portion of petroleum that exists in the semi-solid or
solid phase in natural deposits. It usually contains significant sulfur, metals, and other
nonhydrocarbons. Natural bitumen generally has a density less than 10° API and a viscosity
greater than 10,000 cp measured at original temperature in the deposit and at atmospheric
pressure on a gas-free basis. In its natural viscous state, it is normally not recoverable at
commercial rates through a well and requires the implementation of improved recovery methods
such as steam injection. Near-surface deposits may be recovered using open-pit mining methods.
Bitumen accumulations are classified as unconventional because they are pervasive
throughout a large area and are not currently affected by hydrodynamic influences such as the
buoyancy of petroleum in water. This petroleum type requires upgrading to synthetic crude oil
(SCO) or dilution with light hydrocarbons prior to marketing.
The largest known bitumen resource is in western Canada, where Cretaceous sands and
underlying Devonian carbonates covering a 30,000-sq mile area contain over 1,700 billion bbl of
bitumen initially-in-place (Alberta Energy Resources Conservation Board 2009). Current
commercial developments are confined to the oil sands. Depending on assumed technology
developments and associated economics, estimates of technically recoverable volumes range
from 170 to more than 300 billion bbl (Alberta Energy Resources Conservation Board 2009).
According to the World Energy Council (2007), outside of Canada, 359 natural bitumen
deposits are reported in 21 countries. The total global volumes of discovered bitumen initially-inplace are estimated at 2,469 billion bbl.
8.3.2 Reservoir and Hydrocarbon Characteristics. Individual sand beds in the western Canada
oil sands can form thick and continuous reservoirs of up to 250 ft with a net/gross ratio of over
80%. More often, there are a stacked series of 50- to 150-ft thick sands with intervening silts and
clays. It is common for the sands to have high porosity (30–34%) and permeability (1–5 darcies).
The sand grains are often floating in bitumen with minor clay content. Western Canadia oil sands
may contain a mixture of bitumen, extra-heavy oil, and heavy oil, whose properties differ
between and within reservoirs.
8.3.3 Extraction and Processing Methods. Two general processes are used to extract the
western Canada bitumen: open-pit surface mining and various subsurface in-situ recovery
methods.
In surface mining, the overburden is removed and the oil sands are excavated with very large
“truck and shovel” operations. The oil sands are transported to a processing plant where the ore
Guidelines for Application of the Petroleum Resources Management System
132
is subjected to a series of hot water froth floatation and/or solvent processes to separate the sand
and bitumen. At current economics, typically about 4 tonnes of material are mined to recover 2
tonnes of oil sand ore, which yields 1.2 bbl of bitumen. While the process can recover more than
95% of the bitumen in the sand, the intermixing of clays and the mine-layout requirements
combine to yield approximately an 80% recovery factor. Surface mining is typically considered
where the depth to the top of the oil sands is less than 215 ft. In Canada, approximately 34 billion
bbl is considered recoverable with current surface-mining technology (Alberta Energy Resources
Conservation Board 2009). If all expansions and planned new projects proceed, the total
production from mined bitumen could increase from 600,000 BOPD in 2009 to 1,200,000 BOPD
by 2012.
Bitumen that is too deep for surface mining is typically produced using in-situ thermal
recovery processes similar to those used in heavy oil projects. In general, such projects require a
reservoir depth in excess of 500 ft to provide an impermeable cap to contain the required steam
pressure that provides adequate reservoir energy and temperature. In cyclic steam operations, a
volume of steam is injected into a well, some period of time (soak time) is allowed to pass, and
then the bitumen, whose viscosity has been significantly reduced by the high-temperature steam,
is produced from the same well. This process can be repeated multiple times in the same well
and the recovery efficiency in these projects is typically estimated to be 25 to 30% of the oil
initially-in-place.
Most of the new in-situ projects employ a process termed steam-assisted gravity drainage
(SAGD) using a pair of vertically offset horizontal wells. The upper wellbore is used for steam
injection, creating an expanding steam chamber. The thermally mobilized bitumen drains into the
lower wellbore from which it is produced. A typical project uses well pairs with horizontal
lengths of 2,500 to 3,500 ft, and the injector is placed about 15 ft above the producer. The wells
are drilled in patterns from pads consisting of 5 to 10 well pairs spaced 300 to 500 ft apart.
Expected production rates are 800 to 2,000 BOPD per well. Recovery efficiencies range from 40
to 75% of oil initially-in-place (Etherington and McDonald 2004).
In Canada, the total rate from all current and planned in-situ projects is forecast at 1,500,000
BOPD. Research on improved in-situ processes continues, including use of vaporized solvent
rather than steam to decrease bitumen viscosity (VAPEX), a combination of steam and solvents
called ES (expanding solvent)-SAGD, and a modified fireflood technology. Firefloods are
processes for extracting additional oil by injecting compressed air into the reservoir and burning
some of the oil to increase the flow rate and recovery.
8.3.4 Assessment Methods—Risks and Uncertainties. Bitumen, due to its density and
immobile character, may require different methods to delineate deposits and estimate in-place
volumes than those used for other conventional oil assessments. Conventional production decline
and material balance calculations do not apply.
For surface mine planning, a closely spaced grid of core holes is required to support a
detailed volumetric assessment. The total cores are analyzed in laboratories to determine the
weight percent of bitumen, which is typically 10 to 14 wt% (equivalent to 65 to 89% So). The
Alberta Energy Resources Conservation Board (2001) has published criteria for reporting
mineable resources. The Reserves classification is usually tied to the core grid spacing that
defines continuity. For example, Proved Reserves may require a 1,600-ft grid (61-acre spacing)
while Probable Reserves would be assigned to areas with a 3,200-ft grid (247-acre spacing).
Thickness and condition of overburden, and volume allowances on the lease for mine layout and
Unconventional Resources Estimation
133
tailing ponds are examples of key factors affecting mine economics that would likely be
unfamiliar to engineers focused on conventional reservoirs.
The assessment methods for in-situ bitumen-production operations require close well
spacing and core analysis but are supplemented by high-resolution 3D-seismic and completewireline log suites. Thermal processes, such as SAGD, are sensitive to reservoirs with associated
gas and/or top or bottom water zones that may act as potential thief zones. Water zones rob the
steam chamber of energy otherwise available to heat the bitumen and result in higher operating
costs and poorer oil recoveries.
8.3.5 Commercial Issues. Raw bitumen is marketed at a discount to conventional petroleum at
prices ranging from 25 to 85% of West Texas Intermediate (WTI) benchmark prices depending
on oil quality and seasonal demand. Thus, many projects include integrated or third party
upgrading to yield Synthetic Crude Oil (SCO) that is valued at prices approximating WTI crude.
Bitumen operations are energy intensive and associated greenhouse gases are typically much
greater than for conventional operations. As such, any legislation that taxes emissions may
negatively impact the economics of bitumen projects.
8.3.6 Classification Issues. Similar to improved-recovery projects in conventional reservoirs,
Reserves attribution requires “successful testing by a pilot project, or the operation of an
installed program in the reservoir, that provides support for the engineering analysis on which
the project or program was based.” The difference in bitumen projects is that there may be no
preceding “primary” production upon which to base improved recoveries. However, as more
SAGD projects have come on-stream, the performance results in adjacent analog reservoirs may
be accepted to help underpin the booking of undeveloped reserves.
Under PRMS, to be classed as Reserves, owners must have committed to an approved
development plan including facilities to produce, process, and transport the products to
established markets. It would be difficult to apply all classical petroleum reserves criteria such as
oil/water contacts and offset-well pressure response to unconventional deposits like the Canadian
oil sands. The appropriate assessment methods may be a hybrid of those applied to conventional
petroleum reservoirs and to mining deposits.
In Canada, the Society of Petroleum Evaluation Engineers (SPEE) has created the Canadian
Oil and Gas Evaluation Handbook (COGEH 2007) that is referenced for technical guidance in
Canada’s petroleum disclosure rules. COGEH Vol. 3 provides more-detailed best practices for
bitumen reserves and resources assessment and classification.
References
Alberta Energy Resources Conservation Board. 2009. Alberta’s Energy Reserves 2008 and
Supply/Demand Outlook 2009–2018. ERCB ST98-2009.
Canadian Oil and Gas Evaluation Handbook (COGEH). 2007. Calgary, Alberta: Society of
Petroleum Evaluation Engineers.
Dusseault, M.B. 2001. Comparing Venezuelan and Canadian Heavy Oil and Tar Sands. Paper
2001-061 presented at the Petroleum Society’s Canadian International Petroleum
Conference, Calgary, 12–14 June.
Energy Information: “Survey of Energy Resources 2007,” World Energy Council, Energy
Information
Centre
website:
http://www.worldenergy.org/publications/
survey_of_energy_resources_2007/620.asp
Guidelines for Application of the Petroleum Resources Management System
134
Energy Resources Conservation Board. 2001. Interim Directive ID 2001-7, Operating Criteria:
Resources Recovery Requirements for Oil Sands Mine and Processing Plant Sites.
http://www.ercb.ca/docs/ils/ids/pdf/id2001-07.pdf.
Etherington, J.R. and McDonald, I.R. 2004. Is Bitumen a Petroleum Reserve? Paper SPE 90242
presented at the 2004 SPE Annual Technical Conference and Exhibition, Houston, 27–29
September. DOI: 10.2118/90242-MS.
8.4 Tight Gas Formations
Roberto Aguilera
8.4.1 Introduction. The US Gas Policy Act of 1978 required in-situ gas permeability to be equal
to or less than 0.1 md for the reservoir to qualify as a tight gas formation (TGF) (Kazemi 1982,
Aguilera and Harding 2007). For purposes of this section, the definition is expanded such that a
TGF is “a reservoir that cannot be produced at economic flow rates nor recover economic
volumes of natural gas unless the well is stimulated by a large hydraulic fracture treatment or
produced by use of a horizontal wellbore or multilateral wellbores” (Holditch 2006). The
industry generally divides TGFs into (1) basin-centered gas accumulations (BCGA), also known
as continuous gas accumulations (Law 2002; Schmoker 2005) and (2) gas reservoirs that occur in
low-permeability, poor-quality reservoir rocks in conventional structural and stratigraphic traps
(Shanley et al. 2004). Both types of accumulations can be treated within the PRMS guidelines
given the following minor glossary amendment: “Unconventional TGF resources can exist in
petroleum accumulations that are pervasive throughout a large area and that are generally, but
not always, affected by hydrodynamic influences.”
8.4.2 Resource Potential. The tight gas initially-in place (TGIIP) in the US lower 48 states is
estimated at 5,000 Tscf (Holditch 2006). The estimated recoverable resource is 350 Tscf, which
represents only 7% of the TGIIP. The TGIIP in Canadian TGFs is estimated at 1,500 Tscf
(Canadian Society for Unconventional Gas, Masters 1984). Application of the same recovery
estimate of 7% presented above leads to a resource of 105 Tscf. The bulk of tight gas resources
in Canada is stored in a BCGA in the Western Canadian Sedimentary basin (WCSB). Globally,
the gas resource in TGFs is conservatively estimated at over 15,000 Tscf (Aguilera et al. 2008).
8.4.3 Reservoir and Hydrocarbon Characteristics. The primary definition used in this report
assumes that TGFs, including sandstones and carbonates, are characterized by permeabilities of
less than 0.1 md. The hydrocarbons in these rocks are primarily methane with some impurities,
but there are also occurrences of associated gas condensate.
Permeability is not the only factor that plays a role in gas production from tight gas
reservoirs. A cursory examination of the pseudo steady-state, radial flow equation illustrates that
gas rate is a function of many other physical factors, including pressure, fluid properties,
reservoir and surface temperatures, net pay, drainage and wellbore radius, skin, and the nonDarcy constant (Holditch 2006). Furthermore, a tight gas reservoir can be deep or shallow, high
or low pressure, high or low temperature, naturally fractured, contained within a single layer or
in multiple layers (Holditch 2006), continuous BCGA without a water leg (Law 2002; Schmoker
2005), or with characteristics of a conventional trap under hydrodynamic influences (Shanley et
al. 2004; Aguilera et al. 2008). To succeed and improve recoveries from TGFs, it is necessary to
identify the location and preferential orientation of natural fractures, to distinguish clearly
between water and gas-bearing formations, to efficiently drill into and stimulate multiple
Unconventional Resources Estimation
135
zones,and to enhance the connectivity between wells and their associated drainage volumes
(Kuuskra and Ammer 2004).
Continuous gas accumulations, or BCGAs, are defined (Schmoker 2005) by the US
Geological Survey as “those oil or gas accumulations that have large spatial dimensions and
indistinctly defined boundaries, and which exist more or less independently of the water
column.” In addition, they commonly have low matrix permeabilities, are in close proximity to
reservoir rocks, have low recovery factors (Schenk and Pollastro 2002), and are visualized as “a
collection of gas charged cells.” All of these cells are capable of producing gas, but their
production capabilities change from cell to cell, with the highest production being obtained from
cells with connected natural fractures and/or higher matrix permeabilities.
There are four key elements that define a BCGA:
• Abnormal pressure
• Low permeability (generally ≤ 0.1 md)
• Continuous gas saturation
• No down-dip water leg
If any one of these elements is missing, the reservoir cannot be treated as a continuous gas
accumulation. Note that lithology is not part of the four requirements listed above; the same four
elements have been reported for both clastic and carbonate reservoirs.
Conventional Tight Gas Traps. An opposite view to the concept of continuous gas
accumulations discussed in the previous section has been presented by Shanley et al. (2004).
These authors state explicitly that “low-permeability reservoirs from the Greater Green River
basin (GGRB) of southwest Wyoming are not part of a continuous-type gas accumulation or a
basin-center gas system in which productivity is dependent on the development of enigmatic
sweet spots. Instead, gas fields in this basin occur in low-permeability, poor-quality reservoir
rocks in conventional traps.”
The model Shanley et al. use to explain their theory is called “permeability jail.” The concept
was developed originally by A. Byrnes of the Kansas Geological Survey based on laboratory
work conducted at room conditions and at 4,000 psi overburden stress (Shanley et al. 2004). The
“permeability jail” concept indicates that a range of saturations exist, within which the relative
permeabilities to gas and water are equal to zero; that is, the relative permeabilities do not cross
each other as in the case of conventional reservoirs.
The controversy over whether these accumulations are basin-centered or in low-permeability
conventional traps is important because the estimates of gas-in-place volumes and mobile gas are
much larger in a basin that contains a BCGA instead of discrete conventional traps.
8.4.4 Assessment Methods. The integration of geoscience and engineering aspects are of
paramount importance in exploring for and assessing TGFs. Folding, faulting, natural fracturing,
in-situ stresses, multilayer systems, mineralogy and petrology, connectivity and continuity,
permeability barriers, and interbedded coals and shales are just some of the aspects that must be
taken into account when evaluating TGFs (Aguilera et al. 2008). These are affected by the
dominating tectonics, which in the case of the Rocky Mountain basins are wrench/extensional,
while in the Western Canadian Sedimentary basin they are compressional (Zaitlin and Moslow
2006).
Exploration methods focus on how to locate swarms of natural fractures, positive closures,
and “sweet spots” of higher matrix permeability. Once these are located and natural fracture
orientations are determined, wells are drilled in a way that intercepts the natural fractures.
Inducing formation damage must be avoided as much as possible, which generally implies the
Guidelines for Application of the Petroleum Resources Management System
136
use of underbalanced drilling. However, even if the reservoir is not damaged, stimulation(s) of
the TGF will likely be required to establish commercial production. To be Commercial under
PRMS guidelines, in addition to technical development feasibility, the project must include
economic, legal, environmental, social and governmental viability.
Seismic velocity reductions can indicate zones of high porosity, while variations in seismic
velocity with direction (azimuthal anisotropy) can be related to fractures in the rocks. Wide
azimuth seismic acquisition and processing techniques allow the detection of natural fractures,
which appear as wavy or sinusoidal reflectors on the seismic data. The recognition of fractures,
slots (Byrnes et al. 2006a and 2006b), and the best porosities allows optimum positioning of
drilling targets and, consequently, a reduction in capital and operating costs (Aguilera and
Harding 2007; BP 2008).
This 3D-seismic approach has been used in a large-scale survey in the Wamsutter gas field in
Wyoming (USA), which covers an area of around 4,000 km2. The reservoir section has a
thickness of approximately 600 m and is made up of thousands of very thin gas pay zones. It is
also being used for evaluation of tight gas sands in the In Amenas and In Salah fields in Algeria
and in the Khazzan and Makarem gas fields in Oman (BP 2008).
Ant tracking (Pedersen et al. 2002) is another approach that offers hope for locating fracture
swarms. The technique has been found to be useful for automatic determination of fault surfaces
from conditioned fault-enhancing attributes. In those instances where the fractures are fault
related, the method can provide indirect indications of where the fractures are located.
An integrated approach using shear wave splitting, P-wave azimuthal velocity anomalies,
cores, image logs, and geomechanical methods (Billingsley and Kuuskraa 2006) has proven
useful for locating natural fractures in three distinct geologic settings and tight gas basins in the
US: the Piceance and Wind River basins in the Rocky Mountains, and the Anadarko basin in
western Oklahoma. Under favorable conditions, this technology allows fracture density and
apertures to be estimated. This technology has been reported to improve ultimate recoveries
significantly in lenticular gas plays of the Rulison field in the Piceance basin from 0.9 Bcf/well
in 1956–1972 to 2.0 Bcf/well more recently. The number of dry holes has also dropped from
45% to a low percentage (Billingsley and Kuuskraa 2006).
Hydrodynamic studies must be conducted to determine if the TGF is over- or underpressured,
whether it has a down-dip water leg, if it is continuously gas saturated, and what the approximate
size of the TGF is. This work is useful in determining whether the TGF is a continuous
accumulation (BCGA) or a conventional structural or stratigraphic low-permeability trap. This
work is also very important in planning the development strategy of the reservoir. If the TGF is a
continuous gas accumulation, large problems with water production probably will not be an
issue. However, if the hydrodynamic study shows the presence of a down-dip water leg, it is
reasonable to anticipate that eventually there will be water production problems.
Although porosities are lower in TGFs, this does not necessarily translate into lower
calculated gas saturations. The reason for this is that there are lower values of the Archie
cementation exponent, m, in TGFs resulting from the presence of fractures and slot pores
(Aguilera 2008). The recovery efficiency, however, would be lower than in a conventional gas
reservoir due to the low matrix permeabilities.
An excellent and valuable compilation of rock properties for the Mesaverde Group has been
published by Byrnes et al. (2006a, 2006b) for the Green River, Piceance, Powder River, Sand
Wash, Uinta, Washakie and Wind River basins in the Rocky Mountains region of the US.
Included in their work are routine in-situ porosity, permeability, and grain-density
Unconventional Resources Estimation
137
measurements, along with special core analyses, including cementation and saturation exponents,
cation exchange capacities, mercury injection capillary pressures, drainage critical gas
saturations, thin sections, and core descriptions. Ideally, the same type of information should be
collected for all TGFs, along with the most recent generation of well logs, including image logs
and nuclear magnetic resonance (NMR) logs.
The work of Byrnes et al. (2006a, 2006b) also shows that the value of m becomes smaller as
porosity decreases. They relate the low values of m to the presence of slot pores in TGFs, and
state that “this pore architecture is similar to a simple fracture that exhibits cementation
exponents near m = 1.” The slot porosity can be visualized as grain bounding fractures that result
from uplifting and cooling (Billingsley and Kuuskra 2006).
Well testing, planning, and analysis require specialized methods because of the very low
permeabilities of TGFs. Methods for single- (Lee 1987) and dual-porosity reservoirs (Shahamat
and Aguilera 2008) are available for this purpose. The special signature of gas production
decline can be analyzed with specialized techniques (Arevalo-Villagran et al. 2006; Palacio and
Blasingame 1993) that under favorable circumstances permit estimating permeability and
volumes of gas-in-place with a flowing-gas material balance (Rahman et al. 2006). Specificpurpose type curves can be developed in some instances based on the tight gas productiondecline history of TGFs. Given that well spacing is smaller in tight gas reservoirs than in
conventional reservoirs, single-well simulators can provide reasonable results.
Decline-curve analysis using normalized gas rates can provide good results for estimating
performance, if wells have been producing for several years. If normalization is not possible
because of the lack of pressure data, hyperbolic declines can be used with generally reasonable
results. In this case, it is important to constrain the forecasted production time so that estimates
of ultimate recovery are not skewed by very long production periods (a guideline is to consider a
maximum of 30 years).
The TGF can act in some cases as a gas storage facility, while in other cases (e.g., in a
conglomerate) it can act as the commercial delivery medium to the wellbore. This happens
sometimes in the WCSB of Canada, with the Cadomin conglomerate feeding the wellbore. As
the Cadomin pressures drop, the Nikanassin tight sandstone starts feeding gas into the higher
permeability conglomerate (Zaitlin and Moslow 2006).
8.4.5 Drilling, Completion, and Stimulation Issues. Intercepting natural fractures requires
knowledge of fracture(s) strike and dip. The accepted concept in TGFs is that the well must be
drilled perpendicular to the open fractures. If more than one set of open fractures is present, a
properly designed slanted, horizontal, or multilateral wellbore can maximize gas production and
recovery by intersecting as many fracture sets as economically possible.
In conventional drilling, the mud weight is chosen to exceed the reservoir pressure to avoid
potential blowouts. In TGFs, however, mud invasion can result in large values of skin factor
because these formations are highly susceptible to damage. The problem is exacerbated because
of the complex geology of TGFs, which includes natural fracturing (causing fluid leakoff and
potential sand screenouts), folding and faulting (resulting in high stresses that could make
initiation of the hydraulic fractures difficult or impossible), and channel sands and interbedded
coals and shales (resulting in leakoff into cleats or unexpected fracture-propagation paths).
As a result, underbalanced drilling appears as a reasonable approach for drilling TGFs. In
underbalanced drilling, the usual mud is replaced by fluids, such as inert gases and foams, to
make the hydrostatic pressure exerted on the reservoir smaller than the reservoir pressure. This
eliminates fluid invasion through the fractures and, consequently, minimizes damage to the tight
Guidelines for Application of the Petroleum Resources Management System
138
gas formation. Downhole sensors near the drill bit gather and send information to the surface,
which permits the bit to be steered through the best portions of the reservoir, improving the
probability of success (Bennion et al. 1996).
Unfortunately, underbalanced drilling is not a panacea in TGFs because it can sometimes
induce severe nonanticipated damage. Some of the potential problems include (Craig et al. 2002)
fluid retention, adverse rock/fluid and fluid/fluid interactions, countercurrent imbibition effects,
glazing and mashing, condensate dropout, and entrainment from rich gases, fines mobilization,
and solids precipitation.
Hydraulic fracturing jobs (single or multistage) are necessary in most cases in TGFs, even
when drilling slanted or horizontal wells. However, water retention is a big problem in some
TGFs. As a result, many potential fracturing solutions have been attempted in the past, including
fluids such as pure oil, CO2 energized oil, and cross-linked water-based poly-emulsion and
water-based foam (Rahman et al. 2006; Craig et al. 2002).
8.4.6 Processing and Marketing. A general observation based on experience is that where there
is “conventional gas,” there is also “tight gas” (Aguilera et al. 2008). Furthermore, “tight-sand
accumulations should occur in all or nearly all petroleum provinces of the world” (Salvador
2005). As a result, the processing and marketing of tight gas could proceed hand in hand with
that of conventional gas. Stranded gas, both from conventional and unconventional reservoirs
(including TGFs), requires special handling and economic considerations due to the very large
investments required. In all cases the PRMS guidelines would still apply.
8.4.7 Commercial Issues. Economic considerations have to take into account special drilling,
stimulation, and completion practices; and long transient-flow periods that can last for several
years and even decades in some cases prior to finding any reservoir boundary or discovering the
production effect of an offset well. A larger number of wells per unit area are always required in
TGFs compared to conventional reservoirs. In order to move some of the huge tight gas
resources into reserves, efforts need to focus on many technological improvements that have the
potential to reduce costs and increase gas production rates. The handling of liquids, even in
continuous accumulations without down-dip water, is an important consideration that must be
taken into account when producing TGFs in order to optimize production.
8.4.8 Classification and Reporting Issues. The PRMS (classification, categorization, and
definitions) is generally applicable to TGFs, but given the characteristics of TGFs discussed
previously, there are some differences with respect to conventional reservoirs that should be
highlighted, including the following:
1. In spite of low porosities, the volume of gas initially-in-place (GIIP) is generally much larger
in tight gas reservoirs located in BCGAs compared with conventional reservoirs. In fact, the
continuity of BCGAs suggests that the volume of gas they contain is very large. To avoid
being overly optimistic (Schmoker 2005), the “assessment scope needs to be constrained
from that of crustal abundance to resources that might be recoverable in the foreseeable
future.” The gas volume of a BCGA would initially be classified as total PIIP in the PRMS
guidelines. At a smaller scale it could be divided between Discovered PIIP and Undiscovered
PIIP. Although there would be little doubt about the existence of the TGF, the uncertainties
associated with the presence of natural fractures, higher matrix permeability, low values of
water saturation, and the size of individual well drainage areas will all affect whether the
accumulation can progress from Prospective Resources to Contingent Resources and
Reserves.
Unconventional Resources Estimation
139
2. The gas recovery efficiency, as a percentage of the total GIIP in the entire BCGA without a
water leg, is generally much lower (less than 10%) than in a conventional reservoir.
However, the gas recovery efficiency from a given property (lease or license area or study
area) located in a sweet spot within the continuous accumulation can reach 50% or more. The
bulk of the resources are categorized initially as Contingent Resources but can move very
rapidly to Reserves, if the project’s commercial threshold is met. For a given property, it is
also important to remember that generally a small percentage of the wells will contribute to
the bulk of the gas production. This is sometimes known as the “20-80 rule,” whereby 20%
of the wells produce 80% of the gas.
3. With detailed geoscience, engineering, and economic data, this estimate could be classified
into Reserves (category 1P, 2P, and/or 3P) and Contingent Resources (category 1C, 2C,
and/or 3C). The undiscovered gas can be classified as Prospective Resources (category low,
best, and/or high).
4. Once a project satisfies the required commercial risk criteria, if the foreseeable future is
within the suggested guideline of maximum 5 years, the associated Contingent Resources can
be classified as Reserves.
5. Well spacing is smaller in TGFs, compared with conventional reservoirs. Generally, the
smaller spacing is the result of infill drilling when commercial production has been
established in offset wells but there are no indications of well-interference. A good example
is the Jonah field in Wyoming that started at a 160-acre well spacing and is now down to less
than 10 acres per well. The infill wells are an incremental project (or projects) that adds GIIP
and reserves with time. By contrast, in conventional reservoirs, GIIP remains relatively
constant with time and the 1P, 2P and 3P reserves tend to converge, with the 2P remaining
approximately constant, the 3P decreasing, and the 1P increasing with time.
References
Aguilera, R.F., Harding, T., Krause, F., and Aguilera, R. 2008. Natural Gas Production from
Tight Gas Formations: A Global Perspective. Presented at the 2008 World Petroleum
Congress, Madrid, Spain, 29 June–3 July.
Aguilera, R. and Harding, T.G. 2007. State-of-the-Art of Tight Gas Sands Characterization and
Production Technology. Paper CIM 2007-208 presented at the PS-CIM 2007 Canadian
International Petroleum Conference, Calgary, Alberta, Canada, 12-14 June.
Aguilera, R. 2008. Role of Natural Fractures and Slot Porosity on Tight Gas Sands. Paper SPE
114174 presented at the 2008 SPE Unconventional Resources Conferences, Keystone,
Colorado, 10 –12 February. DOI: 10.2118/114174-MS.
Arevalo-Villagran, J.A., Wattenbarger, R.A., and Samaniego-Verduzco, F. 2006. Some Case
Histories of Long-Term Linear Flow in Tight Gas Wells. J Can Pet Technol 45 (3): 31–37.
PETSOC 06-03-01. DOI: 10.2118/06-03-01.
Bennion, D.B., Thomas, F.B., Bietz, R.F. 1996. Low Permeability Gas Reservoirs: Problems,
Opportunities and Solutions for Drilling, Completion, Stimulation and Productions. SPE
paper 35577 presented at the SPE Gas Technology Conference, Calgary, 28 April–1 May.
DOI: 10.2118/35577-MS.
Billingsley, R.L. and Kuuskraa, V. 2006. Multi-Site Application of the Geomechanical Approach
for Natural Fracture Exploration. DOE Award No. DE-RA26-99FT40720, Advanced
Resources International (March 2006).
BP. Unlocking Tight Gas. http://www.bp.com/sectiongenericarticle.do?categoryId=9019302&con
Guidelines for Application of the Petroleum Resources Management System
140
tentId=7035200. Last entered 11 November 2010.
Byrnes, A.L., Cluff, R.M., and Webb, J. 2006. Analysis of Critical Permeability, Capillary
Pressure and Electrical Properties for Mesaverde Tight Gas Sandstones from Western U.S.
Basins. Quarterly Technical Progress Report, Contract No. DE-FC26-05NT42660, US DOE,
Washington, DC (30 June 2006).
Byrnes, A.L., Cluff, R.M., and Webb, J. 2006. Analysis of Critical Permeability, Capillary
Pressure and Electrical Properties for Mesaverde Tight Gas Sandstones from Western U.S.
Basins. Quarterly Technical Progress Report, Contract No. DE-FC26-05NT42660, US DOE,
Washington, DC (30 September 2006).
Canadian Society for Unconventional Gas
Craig, D.P., Ebenhard, M.J., Odegard, C.E., Ramurthy, M., and Mullen, R. 2002. Permeability,
Pore Pressure, and Leakoff-Type Distributions in Rocky Mountain Basins. Paper SPE 75717
presented at the 2002 SPE Gas Technology Symposium, Calgary, 30 April–2 May. DOI:
10.2118/75717-MS.
Holditch, S.A. 2006. Tight Gas Sands. J Pet Technology 58 (6): 86–93. SPE-103356-MS. DOI:
10.2118/103356-MS.
Holditch, S.A. 2001. The Increasing Role of Unconventional Reservoirs in the Future of the Oil
and Gas Business. http://www.spegcs.org/attachments/studygroups/6/Holditch2001.pdf. Last
visited 11 November 2010.
Kazemi, H. 1982. Low-Permeability Gas Sands. J Pet Technology 34 (10): 2229–2232. SPE11330-PA. DOI: 10.2118/11330-PA.
Kuuskra, V.A. and Ammer, J. 2004. Tight Gas Sands Development—How to Dramatically
Improve Recovery Efficiency. Gas Tips (Winter 2004): 15.
Law, B.E. 2002. Basin-Centered Gas Systems. AAPG Bulletin 86, 1891–1919.
Lee, W.J. 1987. Pressure-Transient Test Design in Tight Gas Formations. J Pet Technol 39 (10):
1185–1195. SPE-17088-PA. DOI: 10.2118/17088-PA.
Masters, J.A. 1984. Elmworth–Case Study of a Deep Basin Gas Field: AAPG Memoir 38. Tulsa,
Oklahoma: AAPG.
Palacio, J.C. and Blasingame, T.A. 1993. Decline Curve Analysis Using Type Curves—Analysis
of Gas Well Production Data. Paper SPE 25909 presented at the SPE Joint Rocky Mountain
Regional and Low Permeability Symposium, Denver, 26–28 April. DOI: 10.2118/25909-MS.
Pedersen, S.I., Randen, T., Sonneland, L., and Steen, O. 2002. Automatic 3D Interpretation by
Artificial Ants. Paper G037 presented at the EAGE Annual Conference and Exhibition,
Florence, Italy, 27–30 May.
Rahman, N.M.R., Pooladi Darvish, M., Santo, M.S., and Mattar, L. 2006. Use of PITA for
Estimating Key Reservoir Parameters. Paper CIM-2006-172 presented at the PS-CIM
Canadian International Petroleum Conference, Calgary, 13–15 June.
Salvador, A. 2005. Energy: A Historical Perspective and 21st Century Forecast. AAPG Studies in
Geology, No. 54, Tulsa, Oklahoma: AAPG.
Schenk, C.J. and Pollastro, R.M. 2002. Natural Gas Production in the United States. Fact Sheet
FS-113-01, US Geological Survey (January 2002). http://pubs.usgs.gov/fs/fs-0113-01/. Last
visited March 15, 2007.
Schmoker, J.W. 1995. U.S. Geological Survey Assessment Concepts for Continuous Petroleum
Accumulations. In Petroleum Systems and Geologic Assessment of Oil and Gas in the
Southwestern Wyoming Province, Wyoming, Colorado and Utah, Chap. 13, Version 1.
Unconventional Resources Estimation
141
Shahamat, M.S. and Aguilera, R. 2008. Pressure-Transient Test Design in Dual-Porosity Tight
Gas Formations. Paper SPE 115001 presented at the SPE/CIPC Gas Technology Symposium,
Calgary, 16 –19 June. DOI: 10.2118/115001-MS.
Shanley, K., Cluff, R.M., and Robinson, J.W. 2004. Factors Controlling Prolific Gas Production
From Low-Permeability Sandstone Reservoirs: Implications for Resource Assessment,
Prospect Development and Risk Analysis. AAPG Bulletin (August 2004): 1083–1121.
Zaitlin, B.A. and Moslow, T.F. 2006. A Review of Deep Basin Gas Reservoirs of the Western
Canada Sedimentary Basin. The Mountain Geologist 43 (3): 257–262.
8.5 Coalbed Methane
C.R. Clarkson and G.J. Barker
8.5.1 Introduction. Coal is defined as a “readily combustible rock containing more than 50% by
weight and more than 70% by volume of carbonaceous material formed from compaction and
induration of variously altered plant remains similar to those in peaty deposits” (Schopf 1956).
Coalbed methane (CBM), variously referred to as natural gas from coal (NGC, Canada) or
coalseam gas (CSG, Australia), is generated either from methanogenic bacteria or thermal
cracking of the coal. Since much of the gas generated in coal can remain in the coal, primarily
because of sorption of gas in the coal matrix, coal acts as both the source rock and the reservoir
for its gas. Exploration for and exploitation of CBM resources requires knowledge of the unique
coal-fluid storage and transport processes as well as special processes (well completions and
operations) required to extract commercial quantities of gas.
8.5.2 Global Potential. CBM resources worldwide are immense, with estimates exceeding 9,000
Tscf (Jenkins and Boyer 2008). The primary producing countries include the US, Canada, and
Australia. More than 40 countries have evaluated the potential of CBM. The US has the most
mature production, with commercial production starting in the 1980s. US production of CBM in
2009 was approximately 1.9 Tscf.
8.5.3 CBM Characteristics. CBM reservoirs are generally naturally-fractured, and the majority
of gas storage is by way of sorption because of the immense internal surface area provided by
organic matter within the coal matrix. The transport of natural gas and water to the wellbore is
dictated primarily by the natural-fracture system. The coal matrix has a very low permeability,
and the mechanism of gas transport is generally considered to be due to diffusion (concentrationdriven flow). Gas diffuses from the coal matrix into the natural fractures and moves under Darcy
flow to the wellbore. The production profiles of CBM reservoirs are unique and are a function of
a variety of reservoir and operational factors.
The primary mechanisms for gas storage in CBM reservoirs are: (1) adsorption upon internal
surface area, primarily associated with organic matter, (2) conventional (free-gas) storage in
natural fractures, (3) conventional storage in matrix porosity, and (4) solution in bitumen and
formation water. Note that the term “sorption” is used here to encompass adsorption of gas on
the internal surface area of coal and solvation of gas by liquid/solids in the coal matrix—when
sorption isotherms are measured in the laboratory for establishing gas content, these mechanisms
of storage are typically not distinguished. Generally, free-gas is negligible compared to sorbed
gas storage and is usually ignored in CBM reservoirs because of low fracture-pore volumes and
high water saturations. The exception is for some dry CBM reservoirs, in which free-gas storage
Guidelines for Application of the Petroleum Resources Management System
142
may be more significant (Bustin and Clarkson, 1999; Bustin and Bustin, 2009). Solution gas is
also usually ignored.
Sorbed gas storage is by far the most important storage mechanism in most CBM reservoirs.
High-rank coals have surface areas on the order of 100 to 300 m2/g, whereas conventional
reservoirs typically have surface areas < 1 m2/g. Most of the gas-accessible surface area of the
coal matrix is associated with organic matter whose pore structure is generally dominated by
microporosity, which are pores that are < 2 nanometers in diameter (Sing et al. 1985). The
controls on CBM-matrix pore structure include thermal maturity (rank), organic matter content,
and coal composition (Bustin and Clarkson 1998). The immense ratio of surface area to volume
in the coal matrix means that a large surface area is exposed to attract gas molecules through
molecular forces (dispersion and electrostatic) that in turn cause adsorption to occur.
The adsorption of CBM-reservoir gases is thought to be primarily physical vs. chemical,
meaning that molecular interaction is weak and reversible. Gas is stored in a near-liquid-like
state, with a higher density than compressed gas at typical reservoir temperatures and pressures.
The controls upon sorption, in addition to the organic matter pore structure, include: pressure,
temperature (Levy et al. 1997), moisture (Joubert et al. 1973), thermal maturity (rank) (Levy et
al. 1997), mineral matter content (grade) (Mavor 1996), organic matter composition (Clarkson
and Bustin 1999), and gas composition (Hall et al. 1994). Sorption on coal is a nonlinear
function of pressure and has been modeled using a variety of empirical and theoretical equations.
By far the most commonly applied single-component and multicomponent gas adsorption model
for coal is the Langmuir isotherm (Mavor 1996). The Langmuir equation can be used to estimate
coal-gas content if the coalseams are saturated (i.e., the in-situ gas content is equal to the in-situ
storage capacity), the reservoir pressure and gas composition are accurately known, the free-gas
and solution-gas storage are negligible, and the average coal composition of the reservoir is
known.
The primary mechanisms governing gas flow in coals include pressure-driven flow (modeled
with some form of Darcy’s law) through the fractures and concentration-driven flow (modeled
with some form of Fick’s law) through the coal matrix. Gas and water flow to the wellbore
through a well-defined natural-fracture system called “cleats.” Cleats generally exist as an
orthogonal set; that is, they are perpendicular to each other and also perpendicular (or nearly so)
to the bedding planes. The “face” cleat is better developed and more continuous than the “butt”
cleat, which terminates into the face cleat. Other, subordinate (“tertiary”) fractures may also
occur (Mavor 1996).
Flow in the fractures is often modeled using some form of Darcy’s law, modified in some
instances to account for two-phase flow (gas + water) and non-Darcy flow effects. Note that if
coals are undersaturated (i.e., the in-situ gas content is less than the in-situ storage capacity), they
will need to be dewatered before gas saturation develops in the fractures. In this case, singlephase flow of water will occur through the fractures until the critical desorption pressure is
reached. If the coals are saturated (in-situ gas content = sorbed gas content), then two-phase
flow of gas will occur from the start of production. Absolute permeability in coal is highly
dependent upon the existence, frequency, orientation (relative to current in-situ stresses), height,
and degree of mineral in-filling in the natural-fracture set (Laubach et al. 1998). A common
model for describing cleat porosity and permeability in coal is the matchstick model (Seidle
1992). The permeability is extremely sensitive to fracture aperture, with which it has a cubic
relationship. Any process acting to modify the cleat aperture will have a strong effect on absolute
permeability.
Unconventional Resources Estimation
143
In coal reservoirs, two physical processes will act to change the physical dimension of the
fracture apertures: (1) changes in effective stress and (2) matrix shrinkage. Note that fines
migration may also act to reduce fracture apertures. With Process 1, because the fracture pore
volume is highly compressible with pore volume compressibilities typically on the order of 10–4
psi–1, increases in effective stress because of pore-pressure depletion can cause the fracture
apertures to decrease in width, which in turn causes a reduction in absolute permeability. In
some coal reservoirs, Process 2 will cause the absolute permeability to increase with depletion,
because the coal matrix will shrink during desorption, causing an increase in fracture apertures.
Several analytical models (Palmer 2009) have been developed that predict permeability
changes as a function of (1) effective stress and (2) matrix shrinkage/swelling. Other important
controls on fluid flow through the fracture system include relative permeability effects (changes
in effective permeability to gas and water during dewatering), reservoir pressure, pressure
drawdown, and fluid properties. For some CBM reservoirs, gas properties will change during
depletion not only because of changes in reservoir pressure, but also as a result of gas
composition changes caused by adsorption behavior. For example, in the Fruitland coal fairway
of the San Juan basin, the initial gas composition contained a significant amount of CO2 (10
mol% or more in some areas), which has increased during reservoir depletion to greater than
20%. This occurs because coal has a greater affinity for CO2 than methane, so it gives up CO2 in
greater amounts as the reservoir is depleted.
The coal matrix provides a source of gas to the fractures. If the fracture density is great
enough, and/or the diffusion coefficient is large enough, the matrix may be assumed to be in
equilibrium with the fractures and desorption may be modeled as an instantaneous release of gas
to the fractures. Also, assuming that the pressure in the fracture system dictates the sorbed gas
content in the matrix, an equilibrium sorption isotherm equation, such as the Langmuir equation,
can be used to model the matrix desorption.
In cases where the fractures are more widely spaced and/or the diffusion coefficient is small,
then desorption from the matrix to the fractures is not instantaneous, and may need to be
modeled using either a pseudo steady-state formulation (using an average gas concentration in
the coal matrix that is not equal to that at the fracture face) or a nonsteady-state formulation, in
which concentration gradients in the matrix are modeled. In either case, some form of Fick’s law
for concentration-driven flow (diffusion) is used to model matrix transport.
Because of the unique storage and transport mechanisms associated with CBM reservoirs,
CBM wells can exhibit unusual gas-production profiles. The production characteristics of a
CBM well exhibiting two-phase flow are illustrated using an example from the San Juan basin
(Fig. 8.2).
Guidelines for Application of the Petroleum Resources Management System
144
Gas/Water Rate (Mscf/D or STB/D)
San Juan Basin (SJ 30-6 Unit) Coal Well
10000
4.
2.
1000
3.
5.
100
1.
10
0
1000
2000
3000
4000
5000
#Days On Production
Fig. 8.2—Production profile for CBM well (Fruitland coal) in the San Juan basin. Red markers indicate gas production,
blue markers represent water production.
In this example, during the early dewatering period (1), gas production inclines primarily as
the result of an increase in the effective permeability to gas. Flowing pressures are also rapidly
decreasing during this period, which is referred to as the “negative decline” period. Once the
well has contacted no-flow boundaries (in this case, probably created by offsetting wells), the
well reaches a peak rate (2) and starts to exhibit a normal decline (3). Note that conventional
decline curves cannot be fit to this dataset until several months after peak production is reached
(> 1,000 days after first production). A disturbance in the well-production profile occurs at
around 2,700 days because of a rapid lowering of backpressure associated with the installation of
compression (possibly coupled with restimulation). This rapid backpressure decrease causes an
additional change in effective permeability to gas caused by an alteration of near-wellbore water
saturation and, possibly, absolute permeability (caused by matrix-shrinkage effects). These
changes result in a short negative-decline period (4). Lastly, a terminal-decline period occurs (5)
when, once again, a decline curve may be fit to the data.
Some dry coal wells, such as those in the Horseshoe Canyon play in Alberta, exhibit a more
conventional decline profile, analogous to shallow gas wells. In the Fruitland coal of the San
Juan basin, wells only a few miles from each other may exhibit production characteristics that
are drastically different. Care must, therefore, be taken in the selection of analog reservoirs and
wells for reserves estimation.
Unconventional Resources Estimation
145
8.5.4 Exploration and Development Considerations. Play- and prospect-analysis tools
developed for conventional reservoirs are not directly applicable to CBM or shale reservoirs
(Haskett and Brown 2005; Clarkson and McGovern 2005). The variability of key CBM-reservoir
properties from basin-to-basin and even field-to-field necessitates a more stochastic approach to
CBM exploration. Failure to reach commercial CBM production is often related to lack of
permeability, resulting in subeconomic rates. Sweet spots can occur in CBM plays due to
enhanced natural fracturing and 3D-seismic techniques are currently being adapted to identify
these enhanced permeability areas (Hyland et al. 2010).
For CBM exploration and appraisal, a key step is the design of the pilot program (Roadifer,
2009). Uncertainties associated with production forecasting include relative permeability,
absolute permeability, and the effect of stress and desorption on permeability during depletion,
permeability anisotropy, and multilayer effects. It is for these reasons that pilots are needed
particularly for undersaturated coal reservoirs, where interior wells are bounded by exterior wells
to accelerate dewatering. The interior wells need to achieve significant (commercial) gas rates,
and effective permeability to gas must be established before reserves bookings can be
contemplated. The pilots need to be designed to reduce the uncertainty in key reservoir
parameters and to test various completion/drilling technologies to determine which are most
cost-effective.
The unique CBM properties also impact later-stage development planning. The two-phase
flow nature of most CBM plays means that well spacing, well geometry and well orientation
should be designed to accelerate dewatering, which will, in turn, increase effective permeability
to gas, initiate gas production, and reduce the time to peak gas production. Care must be taken,
however, not to overdrill or overdevelop, leading to pure acceleration with infill drilling. Critical
data gathered during the exploration phase, such as gas contents, isotherm data, pressures
(flowing and shut-in) and effective/absolute permeability data must continue to be collected
during early and sometimes mature stages of development because of the heterogeneity (vertical
and lateral) of CBM plays. Collection of these key data is necessary to informed development
and business decisions.
Surface operations must also be planned carefully to account for production behavior.
Facilities must be designed to dewater coal wells (artificial lift) and to gather, transport (trucking
or water-gathering system), and treat (subsurface or surface disposal) large amounts of water,
particularly in the early life of a field. Compression must be considered to assist with early
dewatering and to optimize well performance. Additionally, because of the potential for evolving
gas compositions during depletion, facilities may be needed to scrub nonhydrocarbon gases
(such as carbon dioxide) to meet market specifications.
8.5.5 Commercial Issues. A primary consideration for commerciality is the resource size,
related to the thickness and gas content of the coals. Depth of the coal is an important factor
affecting both gas content (through pressure and temperature) and absolute permeability, which
generally decreases with depth due to the stress-sensitivity of the coal fracture apertures.
Commercial production of CBM is generally limited to depths < 4,000 ft for this reason. Factors
affecting the timing of first significant gas production (above the economic limit rate in order to
pay out operating costs)—such as degree of undersaturation—will impact commerciality.
Commerciality will also be affected by factors controlling time to peak production and peak gas
rate, such as effective permeability to gas, which changes with saturation and reservoir pressure.
In CBM projects, it is important that (1) infrastructure is sufficient to gather and dispose of
high initial-water volumes; (2) sufficient compression is installed to improve CBM recovery and
Guidelines for Application of the Petroleum Resources Management System
146
assist with well dewatering; (3) artificial lift is planned for and included in operating costs; (4)
facilities are designed to scrub nonhydrocarbon gas from produced gas to meet market
specification (where applicable); and (5) regulatory concerns are addressed.
8.5.6 Unique Assessment Methods/Issues. Methods for the assessment of CBM
resource/reserves have been adapted largely from techniques developed for conventional
reservoirs. Four general methods are applied:
• Volumetric
• Material balance
• Production data analysis (PDA)
• Reservoir simulation
The appropriate application of these methods depends on the phase of development of the CBM
reservoir. Although both volumetric and simulation methods can be applied at all stages of
development, their accuracy will improve with increased data availability. Material balance,
decline curve, and PDA methods can only be applied after a significant amount of production,
flowing pressure, and shut-in pressure data become available.
Volumetrics. Volumetric estimates of CBM reserves is the simplest method, as well as the
most potentially error prone, because of the uncertainty in basic parameters such as recovery
efficiency and parameters in the total gas initially in-place (TGIIP) calculation [such as bulk
volume of the reservoir (Ah), and in-situ gas content]. Estimated ultimate recovery (EUR) may
be obtained from TGIIP simply by multiplying TGIIP by recovery efficiency (Rf). The most
commonly used form of the GIIP equation for coal is (Zuber 1996)
⎛ 43560φ f (1 − S wi )
⎞
+ 1.3597 ρ c Gc ⎟ …………………………………………(8.1)
Gi = Ah⎜
⎜
⎟
B gi
⎝
⎠
where
Gi = GIIP, Mscf
A = reservoir area, acres
h = reservoir thickness, ft
φf = natural-fracture porosity, dimensionless, fraction
Swi = initial water saturation in the natural fractures, dimensionless, fraction
Bgi = initial gas formation volume factor, Rcf/Mscf
1.3597 = conversion factor
ρ c = average in-situ coal-bulk density corresponding to the average in-situ coal
composition, g/cm3
Gc = average in-situ coal-gas content corresponding to the average in-situ coal
composition, scf/ton.
The primary modification to the conventional GIIP equation has been the inclusion of
adsorbed gas content, which requires specialized field- and lab-based techniques to ascertain.
Adsorbed gas cannot be directly detected in-situ using current petrophysical methods. Recently
(Lamarre and Pope 2007), however, a downhole technique based upon Raman spectroscopy was
introduced that may hold promise for gas-in-place determination, if certain rigid conditions are
met. Raman spectroscopy can be used to measure gas in solution (produced water) from which
the partial pressure of methane is obtained. If it can be assumed that the partial pressure of
methane in the coal is equivalent to gas in solution, and if a representative coal isotherm is
Unconventional Resources Estimation
147
available, the gas content of the coal can be determined (Lamarre and Pope 2007). Carlson
(2006) introduced a technique to establish the critical desorption pressure (CDP) of
undersaturated coals through estimation of bubble point pressure of the water, which they
demonstrate to be equal to CDP.
In the derivation of Eq. 8.1, it is assumed that only gas sorbed in the coal matrix and free-gas
stored in the natural-fracture system are contributing to the gas-in-place. In general, the sorbed
gas content within the coal matrix is the dominant contribution to gas-in-place, and free-gas
storage in both the matrix and the fractures is generally considered to be negligible.
It is very difficult to obtain an accurate value for coal-gas content ( Gc ), mainly because of the
heterogeneity of the coal and the difficulty in the use of well-logging to infer gas content.
Fortunately, the Gas Research Institute (GRI) has published excellent guidelines (e.g.,
McLennan et al. 1995) on the proper assessment of in-situ gas content. Recent advances have
been discussed by Clarkson and Bustin (2010).
Both inorganic and organic fractions of coal affect coal density ( ρ c ). Coal seam bulk
densities are related to the volume fraction of each of these components. Because coal contains
more than 70 vol% (50 wt%) of organic matter by definition, it is easy to detect coals using
openhole density logs. Historically, an upper limit of 1.75 g/cm3 has been used as a cutoff in the
identification of coal on the density log, believed to be in part related to the above definition of
coal. However, as pointed out by Mavor and Nelson (1997), using this definition may exclude
the contribution of other organic-rich materials (i.e., carbonaceous shales) from the total grossthickness calculation. One approach to include them is to establish the coal bulk-density upper
limit at zero adsorbed gas content. Using this approach for Fruitland coal samples, the upper
density limit obtained was consistent with those cited by Mavor and Nelson (1997) (2.1 to 2.5
g/cm3).
The reservoir thickness (h) is intended to be coal thickness, after a density cutoff has been
applied. For each coal reservoir, this may be best estimated using a density cutoff corresponding
to zero adsorbed gas content. In the absence of quality density log data, other wireline logs may
be used to estimate coal thickness. Neutron-porosity logs, which can be run in cased hole, may
be used because coals generally have neutron porosities of > 40%. Gamma ray logs must be used
in parallel with other logs, because although gamma ray responses in coal are generally low, this
depends on the uranium content of the coal.
The reservoir area (A) may correspond to artificial or natural boundaries at the well, field,
play, or basin scale. Artificial boundaries include ownership, survey limits, or well interference
(Mavor 1996). Natural boundaries include coal pinchouts, faults, permeability changes, lateral
facies changes, and other geologic variability. Individual coal seams are so thin, it is often
difficult to resolve them and identify their boundaries with 3D seismic. Well-production-data
analysis, material-balance calculations, and simulation history matching may be used to infer
drainage volumes, which, when combined with volumetric information, can be used to estimate
drainage areas.
In Eq. 8.1, the porosity term refers to natural fracture porosity (φf ), which is difficult to
determine quantitatively from core analysis as discussed by Mavor (1996). Initial water
saturation in the fracture system (Swi) is similarly difficult to ascertain from core techniques and
is commonly assumed to be 100%. In most commercial CBM reservoirs, fracture porosity
(generally < 1%) tends to contribute little to the total gas storage, and some error in the estimate
will, therefore, not have a material impact on estimates of GIIP. However, given that this
porosity is initially filled with water, the practical aspect of having 2% fracture porosity instead
Guidelines for Application of the Petroleum Resources Management System
148
of 1% fracture porosity is that twice as much water will have to be moved to dewater the
reservoir.
There are several approaches to estimating Rf (Zuber 1996):
1. Adsorbed gas content calculated at initial (desorption pressure) and abandonment
conditions using the adsorption isotherm
2. Analogy
3. Reservoir simulation
Material Balance. A number of material-balance equations have been developed that include
adsorbed gas storage (King 1993; Jenson and Smith 1997; Seidle 1999; Clarkson and McGovern
2001; Ahmed et al. 2006), but the degree of complexity of the equations increases as free-gas (or
compressed-gas) storage, water and pore volume compressibility, and water production and
encroachment are accounted for. The method developed by King (1993) remains the most
rigorous, although the equations may be difficult to apply in practice because of the need for
iterative calculations. Since 1997, starting with Jensen and Smith’s (1997) work, approximations
have been developed that ease the use of material balance for CBM reservoirs, without
necessarily sacrificing significant accuracy.
Production Data Analysis. The most abundant data collected for CBM reservoirs is gasand/or water-production data, so it is logical to maximize the use of these data for reserves
estimates. Advanced production-data-analysis methods (i.e., production type curves and flowing
material balance) have similarly been adapted to include adsorbed gas storage, and very recently
have been modified to include more-complex CBM-reservoir behavior, such as two-phase flow
(gas + water), nonstatic absolute permeability (caused by effective stress changes or matrix
shrinkage), and multilayer effects (Clarkson et al. 2007; Clarkson et al. 2008; Clarkson 2009;
Clarkson et al. 2009). Maturing CBM fields and recent simulation studies have provided some
guidelines for the appropriate use of empirical production-analysis techniques such as Arps
decline curves for dewatered or dry CBM reservoirs. A comprehensive study by Rushing et al.
(2008) used constant flowing pressure numerical simulation to investigate the impact of many
CBM reservoir properties on decline characteristics.
Reservoir Simulation. Reservoir simulation includes the use of analytical and numerical flow
models that are "calibrated" by history-matching, well production, and flowing and static (shutin) pressures, and are then used to forecast single or multiwell production under a variety of
operational and development scenarios. A variety of commercial simulators now exist for
analyzing CBM-reservoir behavior, including many aspects of the storage and transport
mechanisms unique to CBM. Reservoir simulation may be performed at the single- or multiwell
level. In either case, for reserves-booking purposes, reservoir simulators must be properly
calibrated to existing well performance using proper constraints on static and dynamic data.
8.5.7 Classification and Reporting Issues (Barker 2008). The current practices to classify
CBM resources often use an incremental approach to delineation and development, similar to
that used in the mining industry and the “well spacing” concepts traditionally applied in the
petroleum sector. The basis for this approach is that uncertainty increases as the distance to
known well control increases resulting in a progression from Proved to Probable to Possible
Reserves. Under these concepts, all the Developed reserves are Proved and Undeveloped
reserves may be Proved, Probable or Possible. However, there may be no explicit evaluation of
the range of uncertainty in recovery efficiency for a project. Consequently, CBM projects often
see large reserves growth provided that the overall area is prospective and there is a tendency to
grow reserves toward a 3P value.
Unconventional Resources Estimation
149
This approach can result in a significantly different reserves maturation profile over time
than that experienced in the conventional petroleum industry where the reserves are based on
uncertainty in recovery for the applied project and are expected to trend towards the 2P value.
Moreover, it is important that a direct link between the applied project and the resource estimate
is maintained to ensure compliance with the project-based principles of the PRMS. The
following summarizes the current practices in defining the resource areas:
Contingent Resources. Demonstrated by drilling, testing, sampling and/or logging
hydrocarbon gas content (e.g., coal sample or gas flow) and coal thickness sufficient to establish
the existence of a significant quantity of potentially moveable hydrocarbons (i.e., there should be
data indicating sufficient permeability for flow within the coal seam). Gas rates may be
undemonstrated or uneconomic, gas composition may or may not support marketability,
significant distance from existing well locations that have demonstrated commercial potential,
outside coal fairway or acceptable depth limits (typically 200 to 1000 m) may require as yet
unproven well technology, (e.g., untried stimulation techniques or horizontal/multilateral wells),
outside areas that can be accessed legally (e.g., protected land), development plan immature or
subeconomic, market not assured, lack of approvals.
Reserves. Demonstrated commercial production potential (pilot test), marketable gas
composition and commercial gas content and thickness (coal sample, gas sample), depth within
accepted economic limits within coal fairway (e.g., 200 to 1000 m depending on the area),
development plan feasible, economically viable, market exists, firm commitment to develop
within a reasonable time frame, approvals existing or imminent.
Proved Developed. Applies to the nominal drainage area for producing or nonproducing
wells that are proven to have commercial quantities of recoverable gas. Well spacings will vary
depending on the region. Typical drainage areas per well are reported to be 80 to 320 acres
(Jenkins and Boyer 2008) and up to 550 acres in the Fairview/Spring Gully fields, Bowen Basin,
Australia (King, June 2008).
Proved Undeveloped. Well spacing rules—distance from Proved Developed location
(typically 1 spacing, in some instances this may be increased to 2 well spacings if the
permeability is high and regional experience justifies good lateral continuity of the coals).
Probable. Well spacing rules—distance from Proved location (typically 2 well spacings, but
this may be extended to greater distances between Proved areas if coal geology, coal quality, and
local experience permits).
Possible. Well spacing rules—distance from Probable location (typically 2 well spacings, may
be extended to greater distances if coal geology, coal quality, and local experience permits or
constrained by geological/geographical limits).
The current conventions are also illustrated diagrammatically in Fig. 8.3. The 200 m and 1000
m depth contours are shown, which for this example are intended to represent the vertical limits
of anticipated commercial production. These rules of thumb may be modified by experience or
additional data (e.g., pressure data from observation wells, which supports continuity over
distances and larger well drainage areas).
Guidelines for Application of the Petroleum Resources Management System
150
Permit Boundary
Outside
fairway
10
00
m
200
3P / or
Contingent
Resource
m
2P
Proved Developed Producing
Proved Developed Non-producing
Proved Undeveloped
1P
Probable
Possible
Contingent
Proved Developed Producing Well Location
Proved Developed Non-Producing Well Location
Core hole
Fig. 8.3—Conceptual 1P 2P and 3P Areas used in the CBM Industry (Barker 2008).
Comments on Current Practice. The current practices have several implications. In the
initial period of appraisal or development, substantial 2P reserves growth is often seen because
the full resource potential may not have been captured and/or disclosed. This is understandable
since coal properties can vary substantially over short distances and sufficient data needs to be
gathered to develop confidence in the recovery estimates away from known data. Area is used as
the main variable in recoverable volume uncertainty. The rate of conversion to 1P reserves
implies that what is termed 2P and 3P must have much higher confidence levels than one would
expect compared to conventional reserves estimates. Some practitioners will have sufficient
confidence in the geological and engineering data to “bracket” areas together and so accelerate
this conversion.
In the absence of any further modifying information, using the typical well spacing
conventions, each Proved Developed well can “prove up” a further 8 Proved Undeveloped and
40 Probable locations 8 . The full area can be categorized as 2P reserves if 1/49 (approximately
2%) of the total planned wells were to be successfully drilled and placed on production at
commercial rates. This is premised on establishing that this well group is located in the coal
fairway in terms of laterally continuous coal thickness and sufficient gas content and
permeability.
8
A practice called “bracketing” or ”rubber-banding” is also used with this method that enables areas larger than that
associated with the well spacing conventions to be categorised in a higher confidence resource class and/or category.
For example, Probable areas located adjacent to or between Proved areas may be deemed Proved if, based on the
judgment of the evaluator, there is sufficient certainty in reservoir continuity and coal properties. Similarly, Possible
areas located adjacent to or between Probable areas may be deemed Probable, and the same principles can apply to
Contingent Resource areas.
Unconventional Resources Estimation
151
The full area can be categorized as 1P Reserves after drilling 1/9 (i.e., 11%) of the planned
development wells assuming an even spacing. At this point, all the original Probable and
Possible Reserves have been converted into Proved Reserves. This implies that there is very little
uncertainty in the estimate of recovery, which given the nature of CBM, is unlikely for projects
of any reasonable scale. As a result, the reserves tend to approach the 3P estimate over time and
as wells get drilled. It is also not unusual to see growth in the 3P component as Contingent and
Prospective Resources are converted to Reserves.
The booking of CBM reserves based on the traditional incremental “well spacing” approach
has advantages in that it is a predictable rules-based system, but the following issues should be
considered in its application:
• It is typically based on a “best estimate” outcome for wells in all reserves category and relies
primarily on area to provide a range of uncertainty in the outcome.
• The defined project applied will need to include development and appraisal of the Probable
and Possible areas to define the ultimate project limits for Reserves to be claimed over these
areas. The definition of the project required to develop the 1P, 2P, or 3P scenario may have a
vastly different scale of investment and market requirements, which has implications for
project approvals and the potential exists for noncompliance with the project-based principles
within PRMS. If Reserves are claimed, they must have the necessary degree of operator
commitment.
• The approach may not clearly separate risk (i.e., the likelihood of commercial production
being realized from a given project) and uncertainty (i.e., the uncertainty in the amounts that
will actually be recovered from the applied project).
Application of the PRMS using an uncertainty-based cumulative approach could provide a
better indication of the risks associated with successive expansion projects proceeding and the
uncertainty associated with the recovery of each project. Another advantage of approaching the
problem in this fashion is that the uncertainty analysis lends itself to probabilistic assessment
should this be required, which may yield additional insight.
Each project will have special circumstances and data availability with regards to technical
merits of the project, maturity of the management approvals, marketing certainty, etc., that will
guide the classifications and volume assignments.
References
Ahmed, T., Centilmen, A., and Roux, B. 2006. A Generalized Material Balance Equation for
Coalbed Methane Reservoirs. Paper SPE 102638 presented at the SPE Annual Technical
Conference and Exhibition, San Antonio, Texas, USA, 24–27 September. DOI:
10.2118/102638-MS.
Barker, G.J., 2008. Application of the PRMS to Coal Seam Gas, Paper SPE 117144 presented at
the SPE Asia Pacific Oil & Gas Conference and Exhibition, Perth, Australia, October 2008
Bustin, A.A.M. and Bustin, R.M. 2009. Gas in Box: How Much Producible Gas is in the
Horseshoe Canyon, CSUG, Calgary, Nov. 2009.
Bustin, R.M. and Clarkson, C.R. 1998. Geological controls on coalbed methane reservoir
capacity and gas content. International Journal of Coal Geology (1998): 3–26.
Bustin, R.M., and Clarkson, C.R. 1999. Free Gas in Matrix Porosity: a Potentially Substantial
Resource in Low Rank Coals Coalbed Methane Symposium Proceedings, Tuscaloosa,
Alabama, May 1999, p. 197-214.
Carlson, F.M. 2006. Technical and Economic Evaluation of Undersaturated Coalbed Methane
Guidelines for Application of the Petroleum Resources Management System
152
Reservoirs. Paper SPE 100224 presented at the SPE Europe/EAGE Annual Conference and
Exhibition, Vienna, Austria, 12-15 June.
Clarkson, C.R. 2009. Case Study: Production Data and Pressure Transient Analysis of Horseshoe
Canyon CBM Wells. J Can Pet Technol 48 (10): 27–38.
Clarkson, C.R., and Bustin, R.M. 1999. The Effect of Pore Structure and Gas Pressure Upon the
Transport Properties of Coal: I Isotherms and Pore Volume Distributions. Fuel 78: 1333–
1334.
Clarkson, C.R. and Bustin, R.M. 2010. Coalbed Methane: Current Evaluation Methods, Future
Technical Challenges. Paper SPE 131791 presented at the SPE Unconventional Gas
Conference, Pittsburgh, Pennsylvania, USA, 23–25 February. DOI: 10.2118/131791-MS.
Clarkson, C.R., Bustin, R.M., and Seidle, J.P. 2007. Production-Data Analysis of Single-Phase
(Gas) Coalbed-Methane Wells. SPE Res Eval & Eng 10 (3): 312–331. SPE-100313-PA.
DOI: 10.2118/100313-PA.
Clarkson, C.R., Jordan, C.L., Gierhart, R.R., and Seidle, J.P. 2008. Production Data Analysis of
CBM Wells. SPE Res Eval & Eng 11 (2): 311–325. SPE-107705-PA. DOI: 10.2118/107705PA.
Clarkson, C.R., Jordan, C.L., Ilk, D., and Blasingame, T.A. 2009. Production Data Analysis of
Fractured and Horizontal CBM Wells. Paper SPE 125929 presented at the SPE Eastern
Regional Conference, Charleston, West Virginia, USA, 23–26 September. DOI:
10.2118/125929-MS.
Clarkson, C.R. and McGovern, J.M. 2001. Study of the Potential Impact of Matrix Free Gas
Storage Upon Coalbed Gas Reserves and Production Using a New Material Balance
Equation. Paper 0113 presented at the International Coalbed Methane Symposium,
Tuscaloosa, Alabama, 14–18 May.
Clarkson, C.R., and McGovern, J.M. 2005. Optimization of Coalbed Methane Reservoir
Exploration and Development Strategies Through Integration of Simulation and Economics.
SPEREE 8 (6): 502–519. SPE-88843-PA.
Hall, F.E., Zhou, C., Gasem, K.A.M., Robinson Jr., R.L., and Yee, D. 1994. Adsorption of Pure
Methane, Nitrogen, and Carbon Dioxide and Their Binary Mixtures on Wet Fruitland Coal.
Paper SPE 29194 presented at the Eastern Regional Conference and Exhibition, Charleston,
West Virginia, USA, 8–10 November. DOI: 10.2118/29194-MS.
Haskett, W.J. and Brown, P.J. 2005. Evaluation of Unconventional Resource Plays. Paper SPE
96879 presented at the SPE Annual Technical Conference and Exhibition, Dallas, 9–12
October. DOI: 10.2118/96879-MS.
Hyland, F., Palmai, F., Yu, M., Abaco, C., Ionkina, N., and Cox, W. 2010. Mannville CBM:
Intergrated Approach in the Mikwan Area. Paper CSIG/SPE 138114 presented at the
Canadian Unconventional Resources & Internaitonal Petroleum Conference, Calgary,
Alberta, Canada, 19–21 October.
International Union of Pure and Applied Chemistry (IUPAC). 1994. Pure Applied Chemistry 66
(8): 1739.
Jenkins, C.D. and Boyer II, C.M. 2008. Coalbed- and Shale-Gas Reservoirs,” J Pet Technol 60
(2): 92–99. SPE-103514-MS. DOI: 10.2118/103514-MS.
Jensen, D. and Smith, L.K. 1997. A Practical Approach to Coalbed Methane Reserve Prediction
Using a Modified Material Balance Technique. Paper 9765 presented at the International
Coalbed Methane Symposium, Tuscaloosa, Alabama, 12–16 May.
Unconventional Resources Estimation
153
Joubert, J.I., Grein, C.T., and Beinstock, D. 1973. Sorption of Methane in Moist Coal. Fuel 52:
181–185.
King, G.R. 1993. Material Balance Techniques for Coal Seam and Devonian Shale Gas
Reservoirs. Paper SPE 20730 presented at the 1993 Annual Technical Conference and
Exhibition, New Orleans, 23–26 September. DOI: 10.2118/20730-MS.
King, G. 2008. Origin Energy. Presented at the UBS Sixth Annual Australian Energy and
Resources Conference, Sydney Australia, 18-19 June 2008.
Lamarre, R.A., and Pope, J. 2007. Critical-Gas-Content Technology Provides Coalbed-MethaneReservoir Data. J Pet Technol 59 (11): 108–113. SPE-103539-MS. DOI: 10.2118/103539MS.
Laubach, S.E., Marrett, R.A., Olson, J.E., and Scott, A.R. 1998. Characteristics and Origin of
Coal Cleat: A Review. Intl. J. of Coal Geology 35 (1998): 175–207.
Levy, J.H., Day, S.J., and Killingley, J.S. 1997. Methane Capacities of Bowen Basin Coals
Related to Coal Properties. Fuel 74 (1): 1–7.
Mavor, M.J. 1996. Coalbed Methane Reservoir Properties. In A Guide to Coalbed Methane
Reservoir Engineering. Report GRI-94/0397, Chicago, Illinois: Gas Research Institute.
Mavor, M.J., and Nelson, C.R.: Coalbed Reservoir Gas-In-Place Analysis, Gas Research Inst.
Report GRI-97/0263, Chicago (1997).
McLennan, J.D., P.S. Schafer, and T.J. Pratt. 1995. A Guide to Determining Coalbed Gas
Content, Gas Research Institute Report No. GRI-94/0396, Chicago, Illinois, 1995.
Palmer, I. 2009. Permeability Changes in Coal: Analytical Modeling. Intl. J. of Coal Geology 77:
119–126.
Roadifer, R.D., and Moore, T.R. 2009. Coalbed Methane Pilots – Timing, Design, and Analysis.
SPE Res Eval & Eng 12 (5): 772–782. SPE-114169-PA. doi: 10.2118/114169-PA.
Rushing, J.A., Perego, A.D., and Blasingame, T.A. 2008. Applicability of the Arps Rate-Time
Relationships for Evaluating Decline Behavior and Ultimate Gas Recovery of Coalbed
Methane Wells. Paper SPE 114514 presented at the CIPC/SPE Gas Technology Symposium,
Calgary, 16–19 June. DOI: 10.2118/114514-MS.
Schopf, J.M. 1956. A Definition of Coal. Economic Geology 51 (1956): 521–527.
Seidle, J.P. 1992. Application of Matchstick Geometry to Stress Dependent Permeability in
Coals. Paper SPE 24361 presented at the SPE Rocky Mountain Regional Meeting, Casper,
Wyoming, USA, 18–21 May. DOI: 10.2118/24361-MS.
Seidle, J.P. 1999. A Modified p/Z Method for Coal Wells. Paper SPE 55605 presented at the
SPE Rocky Mountain Regional Meeting, Gillette, Wyoming, USA, 15–18 May. DOI:
10.2118/55605-MS.
Sing, K.S.W., Everett, D.H., Haul, R.A.W, Moscou, L., Pierotti, R.A., Rouquerol, J., and
Siemieniewska , T. 1985. Pure and Appl. Chem. 57: 603-919
Zuber, M.D. 1996. Basic Reservoir Engineering for Coal. A Guide to Coalbed Methane
Reservoir Engineering, Report GRI-94/0397, Chicago, Illinois: Gas Research Institute.
8.6 Shale Gas
Creties Jenkins
8.6.1 Introduction. Shale gas is produced from organic-rich mudrocks, which serve as a source,
trap, and reservoir for the gas. Shales have very low matrix permeabilities (hundreds of
nanodarcies), requiring either natural fractures and/or hydraulic-fracture stimulation to produce
Guidelines for Application of the Petroleum Resources Management System
154
the gas at economic rates. Shales have diverse reservoir properties, and a wide array of drilling,
completion, and development practices are being applied to exploit them. As a result, the process
of estimating resources and reserves in shales needs to consider many different factors and
remain flexible as our understanding evolves.
8.6.2 Resource Potential. The Potential Shale Gas Committee (Potential Gas Agency 2008)
estimates that there are 616 Tscf of technically recoverable shale gas resources in the US. An
estimate by the INGAA Foundation (Vidas and Hugman 2008) places recoverable shale gas
resources at 385 Tscf for the US and 131 Tscf for Canada. A study in 2001 (Kawata and Fujita
2001) estimated that the total initially-in-place shale gas resource base for the world is 16,103
Tscf. Shale gas currently represents nearly 10% of total US gas production and has been
growing rapidly over the past few years. This has fueled work to find and develop similar
reservoirs around the world.
8.6.3 Reservoir Characteristics. Shales are complex rocks that exhibit submillimeter-scale
changes in mineralogy, grain size, pore structure, and fracturing. In thermogenic shale gas
reservoirs (like the Barnett shale), the organic matter has been sufficiently cooked to generate
gas, which is held in the pore space and sorbed to the organic matter. In biogenic shale gas
reservoirs (like the Antrim shale) the organic matter has not been buried deep enough to generate
hydrocarbons. Instead, bacteria that has been carried into the rock by water has generated
biogenic gas that is sorbed to the organics. TOC (Total Organic Content) values are high in
biogenic shales (often > 10 wt%), but relatively low (> 2 wt%) in thermogenic shales where most
of the TOC has been converted to hydrocarbons.
A common feature of productive thermogenic shale gas plays is brittle reservoir rock
containing significant amounts of silica or carbonate and “healed” natural fractures. Relative to
more clay-rich rock, the brittle rock shatters when hydraulically fracture stimulated, which
maximizes the contact area. Thermogenic shales are often referred to as “fracturable” shales
instead of “fractured” shales. In contrast, biogenic shales are commonly less brittle and rely on
the existence of open natural fractures to provide conduits for water and gas production. A
comprehensive suite of data are needed to fully characterize shale gas reservoirs in terms of their
geochemistry, geology, geomechanics, fluid properties, fracture characteristics, and well
performance. Table 8.1 summarizes these data.
Unconventional Resources Estimation
155
TABLE 8.1—DATA NEEDED TO FULLY CHARACTERIZE SHALE GAS RESERVOIRS
Data
Usage
TOC
Provides an indication of source-rock richness and sorption capacity.
Gas content
Includes the volumes of desorbed, lost, and residual gas obtained from the
desorption of core. It is an indicator of the in-situ sorbed gas content.
Sorption
A relationship, at constant temperature, describing the volume of gas that can
isotherm
be sorbed to a shale as a function of pressure.
Gas composition
Used to quantify the percentage of methane, carbon dioxide, nitrogen, ethane,
etc. in the desorbed gas. Used to build composite sorption isotherms.
Rock-eval
Assesses the petroleum-generative potential and thermal maturity of organic
pyrolysis
matter in a shale sample.
Mineralogical
Determines bulk and clay mineralogy using petrography, X-ray diffraction,
analyses
scanning electron microscopy, and similar techniques.
Vitrinite
A value indicating the amount of incident light reflected by the vitrinite
reflectance
maceral. It is a fast and inexpensive means of determining thermal maturity.
Core description
Visually captures lithology, bedding, fracturing, grain size variations, etc.
3D seismic
Used to determine interwell shale properties including lateral extent,
thickness, faulting, and those areas with higher gas saturation and brittleness.
Kerogen types
Used to assess whether rocks are Type I (oil-prone), II (mixed), or III (coal).
Routine core analysis
Includes total porosity, fluid saturations, bulk density, and matrix permeability
(via pressure pulse testing on crushed samples).
Conventional logs
SP, GR, resistivity, microlog, caliper, density, neutron, sonic, and temperature
logs are run to provide thickness, porosity, matrix, and sorbed gas saturations.
Special logs
May include image logs (fractures), NMR logs (free water, bound water, gas
saturation), pulsed neutron and geochemical tools (mineralogy), dipole sonic
(geomechanical properties), spectral GR (clay types), etc.
PressurePressure buildup or injection fall-off tests to determine static reservoir
transient tests
pressure, permeability, skin factor, and to detect fractured-reservoir behavior.
Geomechanical
Young’s modulus and Poisson’s ratio for determining shale brittleness, stress
properties
orientations and magnitudes to predict fracture growth.
Microseismic
Used to assess hydraulic fracture geometries and stimulated reservoir
volumes.
Fracture diagnostics
Treating pressures, closure stress, pumped volumes, flowback volumes, etc.
to determine the quality of a fracture stimulation.
Gas, water rates
Captured daily (preferably) to assess individual well behavior.
Bottomhole pressures
Preferably recorded in closely-spaced increments (every 10 min) early in well
life; can also use surface pressures with wellbore-fluid gradients.
Tracer surveys
Chemical or radioactive tracers to assess which fracture stages are
contributing.
Facilities
Variations in line pressure, etc., that affect producing well rates.
Rate-transient analysis
Decline analysis tool that analyzes production rates and pressures using
various methods to assess EUR, GIP, drainage area, etc.
Numerical modeling
Helpful in understanding reservoir mechanisms, predicting early well behavior,
and estimating EURs and recovery factors.
Decline-curve analysis
Traditionally used to forecast well performance. More reliable later in well life
(after a few years) due to uncertainties regarding b-factor values.
Analogs
May be useful to estimate EURs and recovery factors if a strong correlation
exists between key reservoir parameters of subject and analog reservoir.
8.6.4 Well Performance. Wells have produced gas from shales since the 1820s, and many
studies have been carried out over the past 30 years to understand and predict their performance.
Thermogenic shale gas reservoirs exhibit steep initial declines of 30 to 80% or more in the first
year, followed by a flattening characterized by a decline exponent (b-factor) greater than 1.0.
Guidelines for Application of the Petroleum Resources Management System
156
This decline behavior is evidence that wells are in transient flow. This may persist for many
years depending upon well spacing and permeability. Because the permeability is so low in these
reservoirs, it may be tens of years before pressures begin to decrease substantially away from
hydraulic fractures. As a result, even though up to half the gas initially-in-place in thermogenic
shale gas reservoirs may be sorbed gas, only a small fraction of this gas will be produced over
the life of the well.
Thermogenic shale gas reservoirs are generally found at depths greater than 3,000 ft, and
production is dominated by dry gas held in the pores of the shales. Initial gas rates for fracturestimulated horizontal wells are typically greater than 1 MMcf/D with corresponding EURs of
more than 1 Bcf. Shales that are thermally immature (in the oil or wet-gas window) generally
have lower IPs and EURs due to relative permeability effects and the difficulties related to
moving liquids through the very small pore throats. Biogenic shale gas reservoirs tend to have
significantly lower production rates and EURs than thermogenic shales because of their shallow
depths, lower gas initially-in-place, and the need to dewater the fractures before producing the
sorbed gas.
8.6.5 Drilling and Development. The most important factor behind the rapid expansion in shale
gas development has been advances in drilling and completions technology. Most notable among
these are the use of (1) horizontal drilling, (2) light-sand slickwater fracs, and (3) microseismic.
The impact of these techniques on gas production has been dramatic. Fracture-stimulated
horizontal wells in the Barnett are expected to produce about 3.8 times as much gas over their
lifetime as fracture-stimulated vertical wells, based on a comparison of median well EURs
(Frantz et al. 2005).
These drilling and completion techniques have been adapted and applied to multiple shale
gas developments including Fayetteville, Woodford, Marcellus, and Haynesville. Lateral well
lengths have increased along with the number of stimulation stages that are pumped. It is now
common for laterals to be 5,000 ft long and contain 15 to 20 fracture stages, which substantially
increases the contacted reservoir volume and accelerates drainage. Microseimic is used to
monitor the stimulations to understand fracture geometries and estimate the stimulated reservoir
volume.
Laterals are drilled parallel to each other and oriented perpendicular to the maximum
compressive stress. Typical patterns in a section (640 acres) range from 4 wells (160-acre
spacing) to 8 wells (80-acre spacing) with some pilot projects containing wells spaced at 40
acres. The choice of well spacing depends on multiple factors including gas-in-place,
permeability, and the volume of rock contacted by hydraulic fractures. Laterals are commonly
landed in the most brittle intervals of the shale to more easily initiate fractures and more
intensely fracture-stimulate the rock. Care is taken to avoid structural complexities including
faults with significant displacement and vertically adjacent water-productive units.
8.6.6 Commercial Issues. The greatest successes in shale gas development are realized by
companies that acquire large acreage positions at low cost in locations that eventually become
the core area of a shale gas play. Work begins by assessing the available data and establishing a
lease position in a prospective area. This is followed by the drilling of numerous appraisal wells
and pilot projects, at a total cost that often exceeds USD 100 million, to assess whether shale gas
development will be commercial. Once this is demonstrated, a viable play requires billions of
additional dollars to drill and complete hundreds of development wells. The cost for these can
range from USD 2 to 3 million for a well in the Barnett shale to more than USD 8 million for a
well in the Haynesville shale.
Unconventional Resources Estimation
157
Because the development of any new shale gas play requires climbing up the learning curve,
it is likely that the earliest wells will deliver some of the poorest results. As a result, well
economics may be marginal until technological innovation, increases in operational efficiency,
and economies-of-scale increase production rates and drive down costs. Gas prices also play a
critical role because low prices not only reduce revenue but also reduce available capital, which
slows the pace of development and further diminishes the present value of the project.
Wells in thermogenic shale gas reservoirs produce at very high initial rates and decline
rapidly. This is due to multiple factors, including a reduction in reservoir pressure near the
wellbore, a reduction in permeability as pore pressure decreases, and reductions in fracture
conductivity resulting from proppant crushing, proppant embedment, and fines migration.
Because many wells produce more than half of their total gas within the first two years, drilling
must expand continuously to increase the field gas rate. In shale gas reservoirs dominated by
sorbed gas, such as the Antrim shale, production may be delayed because of dewatering and
more closely-spaced wells may be needed to accelerate this process.
In the early years of development it may not be possible to gather sufficient data to
understand well spacing, drainage areas, and interference issues because wells are drilled at a
wide spacing (often one well per section) just to hold acreage. As infill drilling proceeds, these
issues can be addressed, and it may be advisable to restimulate or redrill early wells using what
has been learned during the initial phase of development.
Initial gas rates and EURs for shale gas wells are highly variable and difficult to predict, with
values often varying by one to two orders-of-magnitude across any given area. Because of the
log-normal distribution of individual well EURs, the top 5% of wells drilled are critical to the
overall economic success of any project. The goal is to understand what makes these wells so
successful and to replicate this in succeeding wells.
8.6.7 Classification of Shale Gas Prospective and Contingent Resources. Shale gas resources
may be estimated deterministically or probabilistically, with best practice being to use both
methods. Prior to discovery, these techniques can be used to generate low, best, and high
estimates of prospective gas resources, which are commonly risked by a chance of discovery
(Pg) and a chance of commerciality (Pc). The difference between the low and high estimates will
likely be very large, reflecting the uncertainty in both gas-in-place volumes and recovery factors.
Data available for this task could include 2D seismic data and information such as logs, cuttings,
mudlogs, and/or cores from wells that passed through the shale on the way to deeper horizons.
Prospective Resources can become Contingent Resources once a well is drilled and a
discovery is made. According to PRMS, a discovery requires that the collected data establish the
existence of a significant quantity of potentially moveable hydrocarbons. This definition reflects
the expansive nature of PRMS, whereby accumulations such as tar sands may be discovered
without flowing oil to the surface. For shales, there are several criteria that should be considered
before an accumulation is declared to be “discovered.” The first is a well test, which may require
fracture stimulation that produces enough gas to the surface to be of commercial interest. The
second is core and log data that provide convincing evidence of a significant volume of
moveable hydrocarbons. The third is identification of a commercially-productive analog with
sufficient similarity to the subject reservoir to conclude that it should be able to produce gas at
comparable rates and recoveries. It is the combined weight of these three criteria that is
important, which means, for example, if the gas flow rate is thousands of cubic feet per day, then
the evidence from core, logs, and analogs needs to be more compelling than if the gas flow rate
is millions of cubic feet per day.
Guidelines for Application of the Petroleum Resources Management System
158
Once the discovery is made, the next decision is whether a project can be defined using
existing technology or technology under development (see Section 2.3). If not, then the
accumulation should be classified as Discovered Unrecoverable Resources. Initially, Contingent
Resources may be placed in the “economic status undetermined” category while wells are being
drilled to evaluate the commercial potential of the play. Contingent Resources should only be
assigned to this category while an ongoing evaluation is taking place. During this time,
contingencies that impede production (such as poor reservoir properties or completions) and/or
contingencies that impede development (such as low gas prices or insufficient capital) may be
recognized. If it is clear that these cannot be overcome, the resources need to be assigned to the
Unrecoverable or Not Viable subclass.
After a sufficient number of wells have been drilled to demonstrate that the project is
technically feasible and a development plan has been generated, economics can be run to
determine whether the project should be placed in the marginal or submarginal Contingent
Resources category. Because projects at this stage have a chance of failure, evaluators can
express the degree of commercial risk by describing the specific contingencies, quantifying the
chance of commerciality, and/or assigning an appropriate Project Maturity subclass (see Section
2.5). Once the gas has been shown to be commercially recoverable under defined conditions for
a given project, and there is a commitment to proceed with development, shale gas Contingent
Resources can be classified as Reserves.
Since shale gas plays extend beyond the limits of conventional traps, the decision regarding
how far away from existing well control Contingent Resources should be assigned can be
difficult. Two guidelines that should be applied in this work are (1) information from seismic
data showing that the shale is a continuous accumulation of similar character extending away
from well control and is not cut by a sealing fault, and (2) indications that reservoir properties
from wells that bound the Contingent Resources area are sufficiently similar to those of the
discovery well that their well performance is expected to be similar.
8.6.8 Classification of Shale Gas Reserves. The most common way to assign Proved Reserves
and Developed Producing Reserves in shale gas reservoirs is through the use of decline-curve
analysis. Horizontal wells start out with a steep initial decline that eventually flattens, often after
a year or more of production. This flattening continues until some terminal decline rate is
attained (commonly less than 5 to greater than 10%), which is extrapolated to the economic
limit. The shape of the decline curve often is based on comparisons of the subject well to similar
wells either in the same shale gas reservoir or in analogous shale gas reservoirs.
A key drawback in the use of decline curves is the uncertainty associated with projecting
well performance in early time. For example, in the Haynesville shale, a well that initially
produces at a rate of 18 MMcf/D may decline to less than 3 MMcf/D after a year of production.
Depending on how much the decline curve is projected to flatten beyond this first year, the bfactor can range from 0 (exponential) to 1.5 (super-harmonic), and the associated reserves can
vary by a factor of two. In these circumstances, it may be reasonable to use a conservative
decline to assign Proved Reserves, and less conservative declines to assign Developed Probable
and Possible Reserves.
To help reduce the uncertainty associated with these early forecasts, rate-transient analysis
and numerical modeling techniques can be applied. Both of these approaches require highfrequency rate and bottomhole-pressure data from producing wells, and detailed information
about the hydraulic fracture stimulation. Other techniques, such as material balance, do not work
very well because the permeability is so low that it is not possible to obtain accurate static
Unconventional Resources Estimation
159
reservoir pressures. No matter which forecasting technique is used, it is good practice to compare
the resulting EURs to the original gas in place volumes to ensure that the resulting recovery
factors are reasonable.
The assignment of Proved Undeveloped Reserves to offset well locations requires reasonable
certainty that these locations will be economically productive and that the reservoir is laterally
continuous with the drilled Proved locations. Lateral continuity is generally not a problem,
unless the shale is cut by a fault, but the large variability in individual well IPs and EURs can
make the assignment of PUDs problematic at distances beyond one development spacing unit
from a producing well. In general, if there is consistency in the initial rates and estimated
ultimate recoveries of producing wells, then it seems reasonable to assign PUDs at a distance of
two or perhaps three development spacings from these wells as long as these PUD locations are
bounded by other PDP wells. If there are a large number of PDP wells (at least 50 to 100), then it
may be possible to apply the statistical techniques described in SPEE Monograph 3 (2010) to
assign PUDs to a much larger area between PDP wells.
Undeveloped Probable and Possible Reserves may be assigned to well locations beyond
PUDs using type curves derived from producing wells. The choice of which type curve to use
depends on a number of factors including area, permeability-thickness, lateral length, and
completion effectiveness. In practice, it seems reasonable to assign Probable Reserves to 2 to 3
drilling locations beyond PUDs, and Possible Reserves to 2 to 3 drilling locations beyond the
Probable Reserves area. However, in making these assignments, a number of factors need to be
considered including (1) the amount of well control, (2) whether reserves are being assigned
between existing wells or beyond existing wells, (3) whether the geological and petrophysical
data indicate that reservoir properties are similar in the Proved, Probable, and Possible areas, and
(4) whether discontinuities such as potentially sealing faults are present. For reporting purposes,
according to PRMS, shale gas reserves can be statistically aggregated up to the field, property, or
project level. Beyond this level, PRMS recommends using arithmetic summation by reserves
category, which may result in very conservative Proved Reserves estimates and very optimistic
3P reserves estimates due to the portfolio effect. Operators should also be cautious in relying on
aggregations if they are supported only by type curve approaches to forecasting individual wells.
References
Frantz Jr., J.H., Williamson, J.R., Sawyer, W.K., Johnston, D., Waters, G., Moore, L.P.,
MacDonald, R.J., Pearcy, M., Ganpule, S.V., and March, K.S. 2005. Evaluating Barnett
Shale Production Performance Using an Integrated Approach. Paper SPE 96917 presented at
the SPE Annual Technical Conference and Exhibition, Dallas, 9–12 October. DOI:
10.2118/96917-MS.
Kawata, Y. and Fujita, K. 2001. Some Predictions of Possible Unconventional Hydrocarbon
Availability Until 2100. Paper SPE 68755 presented at the SPE Asia Pacific Oil and Gas
Conference, Jakarta, 17–19 April. DOI: 10.2118/68755-MS.
Potential Gas Agency. 2008. Potential Supply of Natural Gas in the United States. Golden,
Colorado: Colorado School of Mines.
Society of Petroleum Evaluation Engineers (SPEE). 2010. Guidelines for the Practical
Evaluation of Undeveloped Reserves in Resource Plays, Monograph 3.
Vidas, H. and Hugman, B. 2008. Availability, Economics, and Production Potential of North
American Unconventional Natural Gas Supplies. Washington, DC: Interstate Natural Gas
Association of America Foundation.
Guidelines for Application of the Petroleum Resources Management System
160
8.7 Oil Shale
John Etherington
8.7.1 Introduction. Oil shales are fine-grained sedimentary rocks (shale, siltstone, and marl)
containing relatively large amounts of organic matter (known as “kerogen”) from which
significant amounts of shale oil and combustible gas can be extracted by destructive distillation.
The organic matter in oil shale is composed chiefly of carbon, hydrogen, oxygen, and small
amounts of sulfur and nitrogen. It forms a complex macromolecular structure that is insoluble in
common organic solvents (versus bitumen that is soluble). Because of its insolubility, the
kerogen must be retorted at temperatures of about 500°C to convert it into oil and gas. Oil shale
differs from coal in that the organic matter in coal has a lower atomic H:C ratio and the organic
matter to mineral matter ratio of coal is much greater.
Global oil shale in-place resources are conservatively estimated at 2.8 trillion bbl. The largest
known deposit is the Green River oil shale in the western US, with an estimated 1.5 trillion bbl
of oil originally-in-place. Other important deposits include those of Australia, Brazil, China,
Estonia, Jordan, and Morocco (World Energy Council 2007).
8.7.2 Production Methods and Assessment Issues. All current commercial extraction projects
use surface mining techniques. Oil shales of Estonia are used directly as fuel for power
generation and in cement plants. China and Brazil also have significant oil shale production.
Brazil has developed the world’s largest surface oil shale pyrolysis retort and 2009 production
was about 3,600 BOPD.
Despite very significant research investments in the Colorado Piceance basin deposits since
the 1970s, there is no current commercial production. Initial pilots were based on surface mining
and associated retort facilities. Typical yields were < 1 bbl of hydrocarbon liquids per tonne of
shale. Environmental issues include the disposal of large amounts of processed shale with
associated contaminants and the potential contamination of groundwater.
Recent research has focused on the potential for in-situ conversion process using various
methods to concentrate heat in the reservoir. The assessment techniques are similar to the
mapping of facies and organic content as employed in shale gas assessments. Assuming that the
current production/processing costs do not support economic projects under near-term product
price forecasts, estimated recoverable volumes for identified deposits would be classified as
Contingent Resources—Development Not Viable.
References
Energy Information: “Survey of Energy Resources 2007,” World Energy Council, Energy
Information
Centre
website:
http://www.worldenergy.org/publications/
survey_of_energy_resources_2007/620.asp.
8.8 Gas Hydrates
John Etherington
8.8.1 Introduction. Gas hydrates are naturally occurring crystalline substances composed of
water and gas, in which a solid water lattice accommodates gas molecules in a cagelike structure,
Unconventional Resources Estimation
161
or “clathrate.” At conditions of standard temperature and pressure (STP), one volume of
saturated methane hydrate will contain as much as 164 volumes of methane gas. Gas hydrates
form when gases, mainly biogenic methane produced by microbial breakdown of organic matter,
combine with water at low temperature and high pressure.
8.8.2 Resource Potential. Because of its large gas-storage capacity, gas hydrates are thought to
represent an important future source of natural gas. They bind immense amounts of methane
within seafloor and Arctic sediments. The worldwide amount of methane in gas hydrates is
considered to exceed 10,000 gigatonnes of carbon. This is about twice the amount of carbon held
in all fossil fuels on earth. Other estimates are quoted as 700,000 Tscf (Collett et al. 1971) inplace. The Mackenzie River delta in northern Canada contains some of the most concentrated
deposits. A number of other countries such as Russia, the US, India, Japan, and China also have
substantial marine gas-hydrate deposits.
8.8.3 Production Methods and Assessment Issues. Theoretical production methods involve
either depressurization or downhole heating, but the technology to support commercial
production has yet to be developed. Research projects are underway using exploration seismic
techniques, petrophysical assessment methods, and experimental production. Selected areas have
mapped significant gas hydrate accumulations penetrated while targeting deeper conventional
reservoirs. Such accumulations may be classified as Contingent Resources—Development Not
Viable, or as Currently Unrecoverable in-place volumes.
References
Collett, T.S. et al. 2002. Energy Resource Potential of Natural Gas Hydrates. AAPG Bulletin 86
(11): 1971–1992.
Natural Resource Canada Website. 2007. Gas Hydrates (Modified 2007-12-20). http://gsc.nrcan.gc.ca/gashydrates/index_e.php.
Guidelines for Application of the Petroleum Resources Management System
162
Chapter 9
Production Measurement and
Operational Issues
Satinder Purewal
9.1 Introduction
An underlying principle within PRMS (SPE 2007) is that reserves and resource quantities will be
reported in terms of the sales products in their condition as delivered from the applied
development project at the custody transfer point. This is defined as the “reference point.” The
objective is to provide a clear linkage between estimates of subsurface quantities, measurements
of the raw production, sales quantities, and the product price received. PRMS provides a series of
guidelines to promote a consistent approach in all types of projects.
9.2 Background
The following discussion provides context for application of PRMS guidelines regarding the
linkage of production measurement to resource estimates in both conventional and
unconventional resource projects.
Fig. 9.1 illustrates typical oil and gas production with local or lease processing; the SPE
historical guidance on measurement points was built around such a model with roots in smallscale onshore gas operations.
1
WELL HEAD
SEPARATOR
2
GAS PLANT
Flare &
losses
SEPARATOR GAS
Fuel Gas
Consumed
Crude Oil
Sales
Condensate
Sales
Residue
Gas
Sales
Plant
Liquids
Sales
Non-Hydrocarbon Products
Sulfur, Helium, Nitrogen, Carbon Dioxide,…
Reservoir A
Reservoir B
Figure 9.1—Reference points in a typical oil and gas operation.
A measurement reference point must be clearly defined for each project. It is typically the
sales point or where custody transfer of the product occurs. For conventional oil and gas
Production Measurement and Operational Issues
163
operations, the measurement point can vary. In many operations, it is at the exit valve of the
lease separator (Point 1 in Fig. 9.1). Where gas plants are involved as part of an integrated
project, the measurement point is typically at the plant outlet (Point 2 in Fig. 9.1).
Volumes of oil, gas, and condensate are adjusted to a standard temperature and pressure
defined in government regulations and/or in product sales contracts. Liquid sales products may
be measured as volumes (e.g., barrels of oil with associated density) or in terms of their mass
(e.g., tonnes of oil). Natural gas is measured in volumes (e.g., cubic feet or cubic meters) and
typically sold on a heating-value basis (e.g., Btu). Products are further specified by their quality
and composition (e.g., sweet light crude, less than X% sulfur).
There is a wide range of complexity in processing facilities. “Local plants” may range from
a simple dehydration unit to a sulfur-recovery plant to a liquefied natural gas (LNG) complex or
a bitumen upgrader. The “plant” may be physically located on the producing property or may be
a considerable distance away connected by a pipeline.
The following levels of processing are recognized:
• Level 1: Volumes undergoing purification and physical separation (e.g., separation of
condensate and natural gas liquids (NGLs) and removal of sulfur from sour gas with
subsequent sale of residual dry gas).
• Level 2: Volumes requiring more extensive treatment (e.g., upgrading by coking), where
chemical changes are induced but no nonreservoir quantities are added. Inert gas and
contaminants are also removed in the process.
• Level 3: Volumes undergoing significant chemical change or where nonreservoir quantities
are added (e.g., hydrotreating that adds hydrogen using catalysts to rechain the hydrocarbon
molecule). Inert gas and contaminants are also removed in the process.
In Level 1 projects, the processing is primarily physical separation, and outlet quantities are
portions of the original reservoir petroleum; thus, resource measurements should be given in
terms of the outlet products (Point 2 in Fig. 9.1). If natural gas is sold before extraction of liquids
(wet gas), resource estimates are given in terms of that volume. Any further processing beyond
this reference point, including additional liquid recoveries (e.g., in “straddle plants”) are not to be
reflected in resource quantities.
Typically, a product sales contract (or pipeline constraints) sets maximum limits on the
nonhydrocarbon “contaminants” content on natural gas deliveries. The volume sold may include
some small fraction of nonhydrocarbons (H2S, CO2) as long as that fraction does not exceed
specifications. Then the resource volumes captured in PRMS categories and classifications
would be estimated including the same nonhydrocarbon content as in the sales gas.
In the case of LNG plants, while significant purification and associated fuel-use shrinkage is
involved, there is no intent to chemically alter the gas but only to change its physical state for
transportation. Inert gases and contaminants that must be removed during processing are part of
shrinkage. If condensate or NGLs are extracted during processing and reported, the gas volume
should be adjusted accordingly. Volumes must be adjusted downward for plant fuel
consumption. While output is measured in tons of LNG, associated reservoir estimates are stated
in terms of equivalent purified/shrunk volume of gas.
Levels 2 and 3 may both be considered upstream manufacturing processes. The actual
custody transfer point in integrated upstream projects depends on the legal structure and contract
terms. Where the same corporate entity shares in both the upstream and downstream operations,
it may be necessary to establish the custody transfer point arbitrarily. Production streams should
be physically measured at the plant inlet, or quantities may be estimated from the outlet products
Guidelines for Application of the Petroleum Resources Management System
164
to account for shrinkage (including fuel usage) and additives. For example, in bitumen-upgrading
operations, whereas the coking process involves significant shrinkage, the addition of hydrogen
results in a volume gain. The synthetic oil delivered at the plant outlet is the final upstream sales
product. Where the custody transfer is deemed to be at the upgrader inlet, a virtual inlet price
may be derived through a netback calculation.
This technical analysis must be combined with royalty treatment, regulatory guidance, and
accounting to ascertain the logical measurement point for stating resource quantities. In cases of
fully integrated extraction and processing operations, transfer prices should be calculated to
value quantities correctly at the designated measurement point.
A further issue is the treatment of the nonhydrocarbons; that is, whether they are
contaminants (with disposal costs and/or no net sales value) or byproducts (e.g., sulfur or
helium) that can be sold to produce additional income. There is general industry agreement that
these nonhydrocarbons in excess of sales specifications are not included in resources quantity
estimates; however, income generated by their sale can be used to offset expenses to extract and
process the associated hydrocarbons (subject to applicable regulatory guidance) when
determining economic producibility for PRMS classifications.
Some disclosure jurisdiction may require separate reporting of heavy oil from light/medium
crude. It is not intended to prescribe here granularity of reporting by the oil and gas industry.
9.3 Reference Point
Reference point is a defined location in the production chain where the produced quantities are
measured. It is typically the point of sale, and where custody transfer takes place between the
buyer and seller. Quantitative transfer across the reference point over a fixed period of time
defines sales production volumes.
The reference point may be defined by relevant accounting regulations to ensure that the
reference point is the same for both the measurement of reported sales quantities and for the
accounting treatment of sales revenues. This ensures that sales quantities are stated according to
their delivery specifications at a defined price. In integrated projects, the appropriate price at the
reference point may need to be determined using a netback calculation.
Sales quantities are equal to raw production less nonsales quantities, being those quantities
produced at the wellhead but not available for sales at the reference point. Nonsales quantities
include hydrocarbons consumed as fuel, flared, or lost in processing plus nonhydrocarbons that
must be removed before sale. Each of these may be allocated using separate reference points, but
when combined with sales, they should sum to raw production. Sales quantities may need to be
adjusted to exclude components added in processing but not derived from raw production. Raw
production measurements are necessary and form the basis of engineering calculations (e.g.,
production performance analysis) based on total reservoir voidage.
9.4 Lease Fuel
In hydrocarbon production operations, in-field produced natural gas is often used for plant
operation, mostly for power generation. Substantial savings can be achieved to the operating cost
of a project by avoiding the purchase of alternative supplies of gas or refined fuels such as diesel.
Data records of consumption for fuel, flare, and other operational requirements need to be
kept for operational and reservoir monitoring purposes. These data may also be required by
regulatory bodies.
Production Measurement and Operational Issues
165
Internationally, the gas (or crude oil) consumed in lease operations is usually treated as
shrinkage and is excluded from sales quantities; thus under PRMS, it would normally not be
included in reserves and resource estimates.
Some jurisdictions allow gas volumes consumed in operations (CiO) to be included in
production and reserves because they replace alternative sources of fuel that would be required to
be purchased in their absence. The value of the fuel used is considered to offset the revenue and
operating costs and hence does not fall into either category. Incidental flared gas is not included
in production or reserves. Gas that is used in operations and has been purchased off the lease is
treated as a purchase and is not included in production or reserves. If gas consumed in operations
is included in production or reserves, it is recommended that a footnote be used to indicate that
the volume of gas CiO is included.
Third-party gas obtained under a long-term purchase, supply, or similar agreement for
whatever purpose is excluded from reserves.
9.5 Associated Nonhydrocarbon Components
If nonhydrocarbon gases are present, the reported volumes should reflect the condition of the gas
at the point of sale. Correspondingly, the accounts will reflect the value of the gas product at the
point of sale. Hence, if gas as produced includes a proportion of CO2, the pipeline may accept
sales gas with a limited CO2 content. For example, if produced gas has 4% CO2 and the pipeline
will accept up to 2% CO2, then it is acceptable to design facilities to deliver sales gas to that
specification. Thus, the sales gas volume would include 2% CO2 and reserves dedicated to that
pipeline would be estimated including 2% CO2. In the case where CO2 must be extracted before
sale, and the sales gas contains only hydrocarbon gases, then all categories of reserves should
reflect only the hydrocarbon gases that will be sold.
The treatment of gas and crude oil containing H2S is generally handled in a similar fashion.
For gas containing small quantities of H2S, this may be included in the reserves where the gas is
sold (e.g., for power generation) and the levels are low enough not to require treatment. Whereas
for LNG and processes involving compression where the dangers following stress-crackingembrittlement are important, the H2S must always be totally removed and therefore should be
excluded from reserves.
For high concentrations of H2S (concentrations as high as 90% have been known), the H2S
gas may be separated and converted to sulfur, which can then be sold. In such cases, the natural
gas reserves exclude the H2S volumes, and the sulfur volume may be quoted separately. At
times, prices for sulfur can be low, and stockpiling for future sale is not uncommon.
Under PRMS, the volumes of nonhydrocarbon byproducts cannot be included in any
reserves or resources classification, but the revenue generated by the sale of the nonhydrocarbon
byproducts may be used to offset project operation expenses, potentially allowing for the
recognition of additional reserves resulting from a lower economic limit. In some cases, revenue
from byproducts such as helium or sulfur can be very significant.
9.6 Natural Gas Reinjection
Gas can be injected into a reservoir for a number of reasons and under a variety of conditions.
Gas may be reinjected into reservoirs at the original location for recycling, pressure maintenance,
miscible injection, or other enhanced oil recovery processes and be included as reserves. Gas is
routinely processed in commingled facilities and redistributed for reinjection, but to retain its
reserves status, these volumes should not have moved past the field’s reference point as
Guidelines for Application of the Petroleum Resources Management System
166
described in 9.3. If reinjected gas volumes are to be included in the reserves, they must meet the
normal criteria laid down in the definitions. In particular, they need to be demonstrably economic
to produce once available for production; the proximity of a gas pipeline distribution system or
other export option should be in evidence; and production and sale of these gas reserves should
be part of the established development plan for the field. In the case of miscible injection or other
enhanced recovery processes, due allowance needs to be made for any gas not available for
eventual recovery as a result of losses associated with the efficiencies inherent in the
corresponding process. Normally, these volumes are not included in any PRMS reserves
category. In some cases, the objective of gas injection in a reservoir can be efficient disposal of
the gas; in such cases, no gas reserves should be allocated to reserves.
Third parties may also purchase gas to be used in a reservoir different from where it is
produced for recycling, pressure maintenance, miscible injection, or other enhanced oil recovery
processes. In such cases, for the originator of the gas, gas reserves, production, and sales are
reported in the normal way; for the recipient, however, even if the gas eventually will be sold,
the gas normally would be a purchase of gas, presumably under a long-term purchase agreement,
and such a gas purchase would not be considered as reserves. It should be accounted for as
inventory. When produced, the gas would not contribute toward field production or sales.
Typically, under such circumstances, the field would then contain gas that is part of the original
in-place volumes as well as injected gas held in inventory. On commencing gas production from
the field, the last-in/first-out principle is recommended; hence, the inventory gas would be
produced first and not count toward field production. Once the inventory gas has been reproduced, further gas production would be drawn against the reserves and recorded as
production. The above methodology ensures that the uncertainty with respect to the original field
volumes remains with the gas reserves and not the inventory. An exception to this could occur if
the gas is acquired through a production payment. In this situation, the volumes acquired could
be considered as reserves.
9.7 Underground Natural Gas Storage
Natural gas may be produced from a field and transported through pipelines and injected into an
underground storage (UGS) reservoir for production at a later date. UGS can be used to meet
fluctuations in gas demand profile, which is subject to the seasonal cycle. UGS may also reduce
flaring by storing the gas for later use rather than burning off the evolved gas from the produced
crude stream. The revenue stream from the produced volumes sold should account for the
molecules produced and then stored in another reservoir according to the contracts in place
between the various owners.
9.8 Production Balancing
9.8.1 Production Imbalances (Overlift/Underlift). Production overlift or underlift can occur in
annual records because of the necessity for companies to lift their entitlement in parcel sizes to
suit the available shipping schedules as agreed among the parties. At any given financial yearend, a company will be in an overlift or an underlift situation. Based on the production-matching
of the company’s accounts, production should be reported in accord with and equal to the liftings
actually made by the company during the year, and not on the production entitlement for the
year.
Production Measurement and Operational Issues
167
For companies with small equity interests, where liftings occur at infrequent intervals
(perhaps greater than 1 year), the option remains to record production as entitlement on an
accrual basis.
9.8.2 Gas Balancing. In gas-production operations involving multiple working interest owners,
an imbalance in gas deliveries can occur that must be accounted for. Such imbalances result from
the owners having different operating or marketing arrangements that prevent the gas volumes
sold from being equal to the ownership share. One or more parties then become
over/underproduced. For example, one owner may be selling gas to a different purchaser from
the others and may be waiting on a gas contract or pipeline installation. That owner will become
underproduced, while the other owners sell their gas and become overproduced. These
imbalances must be monitored over time and eventually balanced in accordance with accepted
accounting procedures.
Some points to consider in gas-balancing arrangements:
• In gas swaps, early production from one field may be traded with later production from
another field.
• Take or pay gas means that the production has to be paid for even if it is not “taken” (i.e.,
produced).
There are two methods of recording revenue to the owners’ accounts. The “entitlement”
basis of accounting credits each owner with a working interest share of the total production
rather than the actual sales. An account is maintained of the revenue due the owner from the
overproduced owners. The “sales” basis of accounting credits each owner with actual gas sales,
and an account is maintained of the over- and underproduced volumes (relative to the actual
ownership). The production volumes recorded by the owners will be different in the two cases.
The reserves estimator must consider the method of accounting used, the current imbalances, and
the manner of balancing the accounts when determining reserves for an individual owner.
9.9 Shared Processing Facilities
It is not uncommon in gas production operations that several fields may be grouped to supply gas
to a central processing facility (gas plant) to remove nonhydrocarbons and recover liquids.
Where a company has an equity interest in one or more of the contributing gas fields and also in
the processing facility, the allocation of dry gas and NGLs back to the fields (and reservoirs) for
estimation of reserves can be complex. While not addressed specifically in PRMS, the basic
principle that reserves estimates must be linked to sales products applies. Thus, by measuring the
volumes and components of the gas stream leaving each lease and the equity share in the lease,
the company can calculate its share of the sales products for purposes of reserves. This share is
not affected by the company’s actual equity interest in the gas plant as long as it is greater than
zero. If the company has no equity interest in the facility, it is treated as a straddle plant and
reserves are estimated in terms of the wet gas and the nonhydrocarbon content accepted at the
lease outlet. The allocation of revenues is subject to the contractual agreement among the lease
and plant owners.
When the plant ownership and lease working interest are different, booking may be an issue.
This can be highly complex, but some general points are captured in the following:
1. If the plant is associated with unit production and is unit owned, book residual plus liquids.
2. If the plant is 100% owned by the company sending produced volumes to the facility, then
that company books the volumes processed by the plant as residual plus liquids.
Guidelines for Application of the Petroleum Resources Management System
168
3. If the contract directly stipulates the retention, by the producer, of products through plant
processing, then the volumes are booked according to contract.
4. If plant ownership and lease ownership interests are different, and existing contracts do not
conclusively specify product allocation, the issues may be complex. In this case, where the
trail is not clear, the booking of wet gas is recommended. The asset team responsible for
handling the produced stream is afforded, however, the opportunity to present information
that describes a specific instance in which the booking of residual plus liquids is reasonable
and adheres to applicable contract terms. Where processed volumes are significant, this
reconciliation is required.
9.10 Hydrocarbon Equivalence Issues
9.10.1 Gas Conversion to Oil Equivalent. Converting gas volumes to an oil equivalent is
customarily performed on the basis of the heating content or calorific value of the fuel. There are
a number of methodologies in common use. Before aggregating, the gas volumes first must be converted to the same temperature and
pressure. It is customary to convert to standard conditions of temperature and pressure (STP)
associated with the system of units being used.
In those parts of the industry that report gas volumes in typical oilfield units of millions of
standard cubic feet (MMscf), Imperial Unit standard conditions are 60°F and 14.696 psia (1
atm). Standard conditions in the metric system are 15°C and 1 atm. Normal conditions used in
part of continental Europe are 0°C and 1 atm. Note that care needs to be taken in converting from
std m3 and Nm3 to scf or vice versa, as the conversion factors are different depending on the
temperature and gas composition. For std m3, the factor is generally 35.3xxx, and for Nm3, the
conversion factor is normally 37.xxx (the last three places vary according to the effect of gas
composition on compressibility behavior).
A common gas conversion factor for intercompany comparison purposes is 1 bbl of oil
equivalent (BOE) = 5.8 thousand standard cubic feet (Mscf) of gas at STP (15°C and 1 atm).
Another factor in use, presumably rounded from the above, is 1 BOE = 6 Mscf.
Derivation of the Conversion Factor. First, some facts:
1 Btu
= 1,055.06 J.
1,000 Btu/scf = 1.055 MJ/scf
= 1.055 MJ/scf x 35.3147 m3/ft3
= 37.257 MJ/m3 at STP (15°C and 1 atm).
From Fig. 9.2, an approximate 35°API oil has a heat content of some 5.8 million Btu/bbl. Thus,
1 BOE =
=
=
=
5.8MBtu = 5.8 x 106 x 1,055.06 J
6,119 MJ
164.238 m3 (at 37.257 MJ/m3)
5,800 ft3 (at STP, viz. 15°C and 1 atm).
Hence, the conversion factor 5.8 Mscf/BOE is based on the heat content of approximately a
35°API crude and a gas with a calorific value of 1,000 Btu/scf (37.3 MJ/m3) at STP (15°C and 1
atm).
Production Measurement and Operational Issues
169
A reasonable approximation of 5.8 Mscf/BOE is recommended for gases where the
condition of the gas is dry at the point of sale. Where one field is being converted (or in the case
of a portfolio of fields where a material proportion of the gas is wet or has a calorific value
materially different to 1,000 Btu/scf), it is necessary to calculate a conversion factor for all fields
in the portfolio on the basis of the actual calorific value of each gas at its point of sale. For
convenience, a weighted average conversion factor, based for example on the remaining Proved
Reserves, could be calculated and used for a company with a large number of holdings.
An alternative conversion factor of 5.62 Mscf/BOE is used by some companies reporting in
the metric system of units. It is based on 1000 std m3 of gas per 1 std m3 of oil. This different
factor can possibly be justified by the observation that price parities tend to weigh up oil energy
relative to gas energy, or by picking a lighter-gravity oil as a reference—but what has carried
weight in practice for the users is that 1,000 is a round and extremely convenient number to use
as long as BOE remains a measurement quantity with no market or customer.
A useful formula for changing calorific value from Imperial to metric units at STP (15°C
and 1 atm) is MJ/m3 = Btu/scf × 35.3 scf/m3 × 1 kJ / 0.948 Btu × 1 MJ/1000 kJ.
Another approach for calculation of gas reserves in terms of BOE is described below:
Depending on the type of crude oil and the quality of gas produced from a reservoir, the
BOE factor may vary significantly. It may be possible to estimate BOE factor for each reservoir
separately and then average-weight it with reserves figure to be used for conversion of gas
reserves number in terms of oil equivalent.
If calorific values of gas volumes are not available at gas sales point, multistage PVT
experimental data on gas liberation process as per separation conditions of the field gathering
system may be used. The first step is to calculate the weighted average gross calorific value of
gas based on composition obtained for each stage of separation of gas.
The mole fraction of each component of gas for particular separation pressure obtained from
the multistage PVT study is then multiplied by standard properties of gross calorific value of the
respective component obtained from standard gas properties chart (Gas Processors Suppliers
Association gas properties chart may be used). The calorific value for each component is added,
to obtain the gross calorific value of gas for that particular stage of separation pressure.
• The calorific value for each component in each stage is summed up to obtain the Gross
Calorific Value for that stage of separation
Σ(Component CV) = Gross Stage CV(*)
•
Total calorific value for the gas is then obtained by average weighting the gas obtained from
each stage with Gas Oil Ratio (GOR) numbers obtained from the same multistage PVT data
from the experiment.
Avg. Wt. Gross CV = (Stage 1 CV × GOR1 + Stage 2 CV × GOR2 + ...
+ Stage n CV × GORn) (*)(*)
GOR1 + GOR2 + ... + GORn
The calorific value obtained using these formulas can be cross-checked by taking actual
calorific value measurements of some gas samples from the sales point.
Guidelines for Application of the Petroleum Resources Management System
170
The calorific value obtained by the process described above can be used for estimating BOE
with a more customized approach, by taking into consideration the crude oil characteristics of the
same reservoir (API and Heating value). This will enhance the reporting of gas in terms of oil
equivalent, as a change in BOE factors affects the overall volume of gas in terms of oil.
TABLE 9.1—ABBREVIATIONS
atm
atmosphere= 1.01325 bar = 101 325 Pa
boe
Btu
barrel of oil equivalent
British thermal unit
3
Ft
3
M
Sm
cubic feet
cubic meter
3
Nm
3
Standard cubic meter at 15°C and 1 atm
Normal cubic meter at 0°C and 1 atm
J
kJ
MJ
Joule
3
kilo (10 ) Joule
6
Mega (10 ) Joule
mscf
mmscf
thousand standard cubic feet
million standard cubic feet
scf
standard cubic feet
For further details on the units and conversion factors refer to The SI Metric System of Units
and SPE Metric Standard, SPE, Richardson, Texas (1984), and Chapter. 6, Sec. 6.6.
9.10.2 Liquid Conversion to Oil Equivalent. Regulatory reporting usually stipulates that liquid
and gas hydrocarbon reserves volumes be reported separately, liquids being the sum of the crude
oil, condensate, and NGL. For internal company reporting purposes and often for intercompany
analysis, the combined volumes for crude oil, condensate, NGL, and gas as an oil equivalent
value offer a convenient method for comparison.
Often, the combination of crude oil, condensate, and NGL reserves volumes are simply
added arithmetically to provide an oil equivalent volume. This is normally satisfactory when one
product dominates and the other two streams are not material in comparison. A more correct, but
imperfect, method in terms of value, involves taking account of the different densities of the
fluids.
Further improvement in combining crude oil, condensate, and NGL can be achieved by
considering the heating equivalent of the three fluids and combining accordingly.
The correlation between the Btu heat content of crudes, condensates, fuel oils, and paraffins
in Fig. 9.2 is based on a combination of data from a number of sources: Katz, Table A-1, Basic
data for compounds; EIA/International Energy Annual (1995); and Alaska Dept. of Natural
Resources (April 1997).
Production Measurement and Operational Issues
171
7.000
6.500
MMBtu/bbl
y = 7E-05x2 - 0.0289x + 6.7365
R² = 0.9881
6.000
5.500
5.000
4.500
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
°API
Fig. 9.2—Btu content of crudes, condensates, fuel oils, and paraffins. (Graph provided
through personal communication with Chapman Cronquist.)
References
McMichael, C.L. and Spencer, A. 2001. Operational Issues. Guidelines for the Evaluation of
Petroleum Reserves and Resources, Chap. 3, SPE, Richardson, Texas, USA.
Petroleum Resources Management System 2007. SPE, Richardson, Texas, USA.
Guidelines for Application of the Petroleum Resources Management System
172
Chapter 10
Resources Entitlement and
Recognition
Elliott Young
10.1 Foreword
This chapter is an update to Chapter 9 of Guidelines for the Evaluation of Petroleum Reserves
and Resources published by SPE in 2001. Drawing heavily on the original text, it has been
updated to reflect refinements in generally accepted industry practices commonly used when
determining entitlement to production and recognizable quantities of reserves and resources
under a range of agreement types and fiscal terms. It is not the intent of SPE, or the cosponsors
of the Petroleum Resources Management System (PRMS) (SPE 2007), to comment on the
individual disclosure regulations promulgated by specific government agencies regarding
entitlement to production or the ability to report reserves. As a consequence, emphasis has been
placed on principles for reserves and resources recognition under PRMS and determination of
net quantities, rather than specific government regulations, financial reporting guidelines, or the
classification of Reserves and Contingent Resources into the various certainty categories of
PRMS.
10.2 Introduction
The ability to discover, develop, and economically produce hydrocarbons is the primary goal of
the upstream petroleum industry. Aggressive competition, ever-sharpening scrutiny by the
investment community, and volatility in product prices drive companies to search for attractive
new exploration and producing venture opportunities that will add the greatest value for a given
investment. As a consequence, contracts and agreements for these opportunities are becoming
increasingly complex, further increasing the focus on the ability to recognize reserves and
resources.
Production-sharing and other nontraditional agreements have become popular given the
flexibility they provide host countries in tailoring fiscal terms to fit their sovereign needs while
enabling contracting companies to recover their costs and achieve a desired rate of return.
However, actual agreement terms, including those that relate to royalties or royalty payments,
cost recovery, profit sharing, and taxes, can have a significant impact on the ability to recognize
and report hydrocarbon reserves. This chapter focuses on reserves and resources recognition and
reporting under the more common fiscal systems being used throughout the industry. The various
types of production-sharing, service, and other types of common contracts are reviewed to
illustrate their impact on recognition and reporting of oil and gas reserves and resources in the
context of the PRMS framework.
Resources Entitlement and Recognition
173
Oil and gas reserves and resources are the fundamental assets of producing companies and
host countries alike. They are literally the fuel that drives economic growth and prosperity. When
produced and sold, they provide the crucial funding for future exploration and development
projects. With the sharpening focus of the investment community on reserves and resources
inventories and the value of externally reported, project-related reserves that are added each year,
many companies are reluctant to undertake a project that does not provide the opportunity to
report reserves.
10.3 Regulations, Standards, and Definitions
In defining reserves, it is important to distinguish between the specific regulations that govern
the reporting of reserves externally and internal company use for technical and business-planning
purposes. The term “reserves” is used throughout the industry but has many different and often
conflicting meanings. The explorationist may refer to the reserves of an undrilled prospect, the
engineer refers to the reserves of a producing property, the financial analyst refers to the reserves
of a company, and governments refer to the reserves of the country. Rarely do all these groups
mean the same thing, even though they use the same term. One of the key strengths of PRMS is
the framework it provides to clarify what is being referred to. In any assessment, the basis used,
assumptions, and purpose for which reserves and resources are recognized and reported must be
defined. Fig. 10.1 summarizes the PRMS reserves and resources categories with the reserves
categories that many government regulatory agencies allow in required disclosures. Fig. 10.2
(SPE 1979; Martinez et al. 1987; SPEE 1998) provides a summary of the more widely
recognized regulatory reporting agencies, standards, and technical definitions.
Figure 10.1—PRMS Classification Categories
Reporting to National Regulators
Regulatory Reporting
PRMS Resources
Classifications
Categories
Generally
Allowed in
Regulatory
Reporting
Figure 10.2—Regulations, Standards, and Definitions
Proved
Reserves
Probable
Reserves
Possible
Reserves
C1
Resources
C2
Resources
C3
Resources
US Securities and Exchange Commission
US Financial Accounting Standards Board
International Accounting Standards Board
UK Accounting Standards Board
Australian Securities Exchange
Canadian Securities Administrators
Russian Ministry of Natural Resources
China Petroleum Reserves Office
Norwegian Petroleum Directorate
Technical
SPE/WPC/AAPG/SPEE PRMS
United Nations Framework Classification
Host Country Technical Definitions
10.3.1 Host Government Regulations. Numerous national regulatory bodies have developed
regulations and standards for reporting oil and gas reserves within their respective countries
(Martinez et al. 1987; SEC Guidelines, Rules, and Regulations 1993; FASB 1977; APPEA 1995;
UK Oil Industry Accounting Committee 1991; Johnston 1994). These standards provide detailed
descriptions of the categories of reserves to be reported, required supporting information, and the
format to be used for the disclosures. However, these standards and regulations do not generally
provide much guidance on the type or extent of rights to the underlying resource or production
that is required for reporting. For some unique types of agreements, it may not be clear whether a
company is even entitled to report the related reserves. This is particularly the case with
Guidelines for Application of the Petroleum Resources Management System
174
agreements in which reserve ownership and control resides, by law, with the host country rather
than with the contracting party. Analysis of the key elements and fiscal terms of these contracts
and comparison to those in more widespread use is a good approach to determine whether
reserves and resources can be recognized and subsequently reported.
PRMS recognizes the concept of an economic interest as the basis for recognizing and
reporting reserves and resources. To determine when an economic interest exists, many
companies have referred to the SEC Section S-X, Rule 4-10b, “Successful Efforts Method” (US
SEC 1993) [or Financial Accounting Standard 19 (FASB 1977)]. While Rule 4-10b was revised
in the 2008 SEC rule modernization, the fundamental principles contained in the definition of a
mineral interest provide a very useful framework and criteria for establishing when an interest in
a property exists and guidance on when reserves and resources can be recognized under PRMS
and government regulations:
SEC Section S-X, Rule 4-10b Successful Efforts Method:
Mineral Interests in Properties. Including:
(i) a fee ownership or lease, concession or other interest representing the right to
extract oil or gas subject to such terms as may be imposed by the conveyance of that
interest;
(ii) royalty interests, production payments payable in oil or gas, and other
nonoperating interests in properties operated by others; and
(iii) those agreements with foreign governments or authorities under which a
reporting entity participates in the operation of the related properties or otherwise
serves as producer of the underlying reserves (as opposed to being an independent
purchaser, broker, dealer or importer). Properties do not include other supply
agreements or contracts that represent the right to purchase, rather than extract, oil
and gas.
10.4 Reserves and Resources Recognition
Regulation SEC Section S-X, Rule 4-10b can be summarized into elements that support and
establish an economic interest and the ability to recognize reserves and resources. These include
the following:
• The right to extract oil or gas
• The right to take produced volumes in kind or share in the proceeds from their sale
• Exposure to market risk and technical risk
• The opportunity for reward through participation in producing activities
In addition, the regulation establishes specific elements that do not support an economic
interest and preclude the recognition of reserves and resources. These include the following:
• Participation that is limited only to the right to purchase volumes
• Supply or brokerage arrangements
• Agreements for services or funding that do not contain aspects of risk and reward or convey
an interest in the minerals
Note that the US Financial Accounting Standards Board (Topic 932) permits reporting of Proved
Reserves received under long-term supply agreements with governments, provided that the
enterprise wishing to report the reserves participates in the operation or otherwise serves as the
Resources Entitlement and Recognition
175
operator. Applying PRMS to this type of agreement, recoverable amounts could be classified as
Reserves and/or Resources depending on project maturity and technical certainty.
The right to extract hydrocarbons and the exposure to elements of risk and the opportunity
for reward are key elements that provide the basis for recognizing reserves and resources. Many
companies use these elements to differentiate between agreements that would allow reserves to
be recognized and reported to regulatory agencies from those purely for services that would not
allow recognition of reserves and resources. Risks and rewards associated with oil and gas
production activities stem primarily from the variation in revenues from technical and economic
risks. Technical risk affects a company’s ability to physically extract and recover hydrocarbons,
and is usually dependent on a number of technical parameters. Economic risk is a function of the
success of a project and is critically dependent on the ability to economically recover the in-place
hydrocarbons. It is highly dependent on the economic environment over the life of the project
and fluctuates with the prevailing price and cost structures. It should be noted that risk associated
with variations in operating cost alone is not generally sufficient to fulfill the requirements of
risk and reward and allow reserves to be reported. It should also be noted that the ability or
obligation to report reserves to regulatory agencies does not necessarily imply ownership of the
underlying resources.
10.4.1 Taxes and Reserves. In general, net working interest reserves and resources are
recognized in situations where there is an economic interest, and after deduction for any royalty
owed to others. Production sharing or other types of operating agreements lay out the conditions
and formulas for calculating the share of produced volumes to which a contracting company will
be entitled. These volumes are normally divided into cost recovery and profit volume
components. The summation of the cost and profit volumes that the contractor will receive
through the term of the contract represents the reserves and resources that the contractor is
entitled to. In many instances, these agreements may also contain clauses that provide that host
country income taxes will be paid by the government or the national oil company on behalf of
the contractor. While details on the specific hydrocarbons produced and revenues that are used to
fund the payments are not usually specified in the agreement, they are inferred to come from the
government’s share of production. By virtue of the economic interest that the contractor has in
these additional volumes, common practice is to include the related quantities in the contractor’s
share. This also typically requires reporting of the value related to the tax payment that is
received in the financial reporting statements.
10.4.2 Royalties and Reserves. Royalties are typically paid to the owner of the mineral rights in
exchange for the granting of the rights to extract and produce hydrocarbons. Royalties are a
form of a nonoperating interest in the underlying hydrocarbons that is free and clear of all
exploration, development, and operating costs. They are generally a fixed percentage or may
have some form of a sliding scale basis. Royalty volumes that are payable either in-kind or in
monetary terms to the owner of the mineral rights are normally excluded from net reserves and
resources. However, in many agreements and/or fiscal systems, the wording that describes this
obligation may be in the language of the host country and may not translate well into English. As
a consequence, the defined payments or obligation may, in reality, be an additional form of tax.
While there are no published standards to differentiate between royalties and taxes, examination
of the specific attributes and the intent of the payment or obligation in comparison to other
established and recognized royalties and taxes is one approach often used to make the
distinction. For example, if the obligation is based on project profitability rather than a defined
interest, or costs are deductible from the obligation, an argument can be made that the obligation
Guidelines for Application of the Petroleum Resources Management System
176
has attributes of a tax rather than a royalty. Where the payment is concluded to be a tax, the
related reserves and resources are included in amounts recognized by the contractor.
10.4.3 Mineral Property Conveyances. A mineral interest in a property may be conveyed to
others to spread risks, to obtain financing, to improve operating efficiency, or for tax benefits.
Some types of conveyances are essentially financial arrangements or loans and do not carry with
them the ability to recognize or report reserves or resources. Other forms may involve the
transfer of all or a part of the rights and responsibilities of operating a property or an operating
interest and the ability to recognize reserves or resources. While intended for US SEC reserves
reporting, the following text from the US Financial Accounting Standards Board, Standard 19
(FASB 1977), (paragraph 47a) provides useful guidance on when reserves and resources may be
recognized in PRMS categories.
a) Other transactions convey a mineral interest and may be used for the recognition
and reporting of oil and gas reserves. These types of conveyances differ from
those described above in that the seller’s obligation is not expressed in monetary
terms but as an obligation to deliver, free and clear of all expenses associated
with operation of the property, a specified quantity of oil or gas to the purchaser
out of a specified share of future production. Such a transaction is a sale of a
mineral interest for which the seller has a substantial obligation for future
performance. The purchaser of such a production payment has acquired an
interest in a mineral property that shall be recorded at cost and amortized by the
unit-of-production method as delivery takes place. The related reserves estimates
and production shall be reported as those of the purchaser of the production
payment and not of the seller.
If an agreement satisfies the requirements of FASB Standard 19, Paragraph 47a, the
purchaser of a production payment is able to recognize the related reserves and resources and
would be permitted to externally report the related reserves per applicable regulatory agency
regulations. However, if the agreement is purely a financial arrangement or loan, the purchaser
would not be able to recognize reserves and resources or report them externally. Production
payments have been widely used as a hedging vehicle in periods of price volatility.
10.5 Agreements and Contracts
Agreements and contracts cover a wide spectrum of fiscal and contractual terms established by
host countries to best meet their sovereign needs. Currently, there is no consistent industry
approach or established practice for determining when reserves or resources can be recognized
under the wide variety of these contracts. The purpose of this section is to expand on the text
contained in PRMS 3.3.2 by providing more detailed information for the various agreement
types noted and to promote consistency in the recognition of reserves and resources under them.
The focus is on the specific elements of the agreements that enable recognition of reserves and
resources but not on the classification into specific PRMS certainty categories.
This section follows the classification system template proposed by Johnston (Johnston 1994;
Johnston 1995; McMichael and Young 1997) as shown in Fig. 10.3. This template has also been
expanded to include three additional types of agreements: purchase agreements, loan agreements,
and production payments and conveyances. The expanded template of agreement types along
with their ranking in terms of the ability to recognize reserves and resources and report them to
Resources Entitlement and Recognition
177
regulatory agencies is shown in Fig. 10.4 (McMichael and Young 1997). Key aspects of each
type of agreement are summarized in Table 10.1 (McMichael and Young 1997).
Figure 10.3—Classification of Petroleum Fiscal Systems
Petroleum Fiscal Arrangements
State Ownership
Private Ownership
Concessionary Systems
Contractual Systems
Conveyances
Fee in Cash
Fee in Kind
Service
Contracts
Purchase
Contracts
Production-Sharing
Contracts
Profit-Based
Flat Fee
Loan
Agreements
Pure-Service
Contracts
Risked-Service
Contracts
Revenue-Sharing
Contracts
Increasing Degree of Ownership
Figure 10.4—Spectrum of Petroleum Fiscal Systems
Concessions
Production-Sharing
Contracts
Revenue-Sharing
Contracts
Pure-Service
Contracts
Loan Agreements
Purchase Agreements
Increasing Likelihood of Reserves Recognition
Guidelines for Application of the Petroleum Resources Management System
178
Table 10.1—Contract Summary
Contract Type
Concession
Production Share
Ownership
Payment
Reserves
Contractor
In-Kind
Yes
Contractor
In-Kind
Yes
(When Produced)
Revenue Share
Government
Share of Revenue
Yes
Risked Service
Government
Fee-Based
Likely
Pure Service
Government
Fee-Based
No
Purchase
Government
Product Cost
No
Loan
Government
Interest
No
Conveyance
Government
Production Pmnt
Likely
10.5.1 Concessions, Mineral Leases, and Permits. Historically, leases and concessions have
been the most commonly used agreements between oil companies and governments or mineral
owners. In such agreements, the host government or mineral owner grants the producing
company the right to explore for, develop, produce, transport, and market hydrocarbons or
minerals within a fixed area for a specific amount of time. The production and sale of
hydrocarbons from the concession are then typically subject to rentals, royalties, bonuses, and
taxes. Under these types of agreements, the company typically bears all risks and costs for
exploration, development, and production and generally would hold title to all resources that will
be produced while the agreement is in effect. Reserves consistent with the net working interest
(after deduction of any royalties owned by others) that can be recovered during the term of the
agreement are typically recognized by the upstream contractor. Ownership of the reserves
producible over the term of the agreement is normally taken by the company. However, as
described in PRMS 3.3.3, volumes recoverable after the term of the contract would normally be
classified as resources and be contingent on the successful negotiation of an agreement
extension. If the contract contained provisions for extension and the likelihood of extension was
judged to be reasonably certain, additional reserves would likely be recognized for the length of
the extension period, provided requirements for project commitment and funding were satisfied.
10.5.2 Production-Sharing Contracts. In a production-sharing agreement between a contractor
and a host government, the contractor typically bears all risks and costs for exploration,
development, and production. In return, if exploration is successful, the contractor is given the
opportunity to recover the investment from production (cost hydrocarbons), subject to specific
limits and terms. The contractor also receives a stipulated share of the production remaining after
cost recovery (profit hydrocarbons). Ownership of the underlying resource is almost always
retained by the host government. However, the contractor normally receives title to the
prescribed share of the volumes as they are produced. Subject to technical certainty, reserves in
one or more of the PRMS categories based on cost recovery plus a profit element for
hydrocarbons that are recoverable under the terms of the contract are typically recognized by the
Resources Entitlement and Recognition
179
contractor. Resources may also be recognized for future development phases where project
maturity is not sufficiently advanced or for possible extensions to the contract term where this
would not be a matter of course.
Under a production-sharing contract, the contractor’s entitlement to production generally
decreases with increasing prices because a smaller share of production is required to recover
investments and costs. These agreements commonly contain terms that reduce entitlement as
production rate (production tranches) and/or cumulative production increases (“R” factors). Fig.
10.5 is a schematic indicating the distribution of yearly project production between contractor
and government. As in the case of a concession, volumes recoverable after the term of the
contract would normally be classified as Resources unless the contract contained provisions for
extension and there was continued commitment to the project.
Figure 10.5—Example Production-Sharing Contract
100 Barrels
Contractor
Government
Royalty
Cost
Barrels
% Profit
Barrels
Cost Recovery
Profit Split
Mechanism
(100−%) Profit
Barrels
Total Entitlement
Volume
Taxes
10.5.3 Revenue-Sharing/Risked-Service Contracts. Revenue-sharing contracts are very similar
to the production-sharing contracts described earlier, with the exception of contractor
remuneration. With a risked-service contract, the contractor usually receives a defined share of
revenue rather than a share of the production. The contractor has an economic or revenue interest
in the production and hence can recognize reserves and resources. As in the production-sharing
contract, the contractor provides the capital and technical expertise required for exploration and
development. If exploration efforts are successful, the contractor can recover those costs from
sales revenues. Also similar to a production-sharing contract, resources may be recognized for
future development phases or possible extensions to the contract term.
Fig. 10.6 is a schematic of the distribution of yearly project revenue between contractor and
government. This type of agreement is also often used where the contracting party provides
expertise and capital to rehabilitate or institute improved recovery operations in an existing field
and has rights and obligations and bears risks similar to those in the previously noted agreement
types.
Guidelines for Application of the Petroleum Resources Management System
Figure 10.6—Example Revenue-Sharing Contract
180
Figure 10.7—Example Risked-Service Contract
100 Barrels
100 Barrels
Contractor
Contractor
Royalty
Cost
Recovery
Revenue
% Profit
Revenue
Total
Revenue
Received
Government
Government
In-Kind or
Monetary
Cost Recovery
Revenue Split
Mechanism
Taxes
(100−%) Profit
Revenue
Contractor Payment
Mechanism
In-Kind
Payment
Investment
Component
And / Or
Expense
Component
Monetary
Payment
Remainder
After Contractor
Payment
Performance
Component
Total In-Kind
and/or Monetary
Payment Received
Taxes
Reserves and resources recognized under PRMS and those reported to regulatory agencies
would be based on the economic interest held or the financial benefit received, as shown in Fig.
10.7. Depending on the specific contractual terms, the reserves and resources equivalent to the
value of the cost-recovery-plus-revenue-profit split are normally reported by the contractor.
10.5.4 Pure-Service Contracts. A pure-service contract is an agreement between a contractor
and a host government that typically covers a defined technical service to be provided or
completed during a specific period of time. The service company investment is typically limited
to the value of equipment, tools, and personnel used to perform the service. In most cases, the
service contractor’s reimbursement is fixed by the terms of the contract with little exposure to
either project performance or market factors. Payment for services is normally based on daily or
hourly rates, a fixed turnkey rate, or some other specified amount. Payments may be made at
specified intervals or at the completion of the service. Payments, in some cases, may be tied to
the field performance, operating cost reductions, or other important metrics. In many cases,
payments are made from government general revenue accounts to avoid a direct linkage with
field operations.
Risks of the service company under this type of contract are usually limited to
nonrecoverable cost overruns, losses owing to client breach of contract, default, or contract
dispute. These agreements generally do not normally have exposure to production volume or
market price; consequently, reserves and resources are not usually recognized under this type of
agreement. The service company may, however, have an obligation to report gross (total working
interest basis) reserves and resources to the host countries’ regulatory agencies. Fig. 10.8 is a
schematic of the distribution of yearly project revenue between contractor and government.
Resources Entitlement and Recognition
181
Figure 10.8—Example Pure-Service Contract
100 Barrels
Contractor
Government
Contractor Payment
Mechanism
Expense
Component
Monetary
Payment
Total Payment
Received
Investment
Component
Remainder
After Contractor
Payment
Performance
Component
Taxes
10.5.5 Loan Agreements. A loan agreement is typically used by a bank, other financial investor,
or partner to finance all or part of an oil and gas project. Compensation for funds advanced is
typically limited to a specified interest rate. The lender does not participate in profits earned by
the project above this interest rate. There is normally a fixed repayment schedule for the amount
advanced, and repayment of the obligation is usually made before any return to equity investors.
Risk is limited to default of the borrower or failure of the project. Variations in production,
market prices, and sales do not normally affect compensation. Reserves and resources would not
be recognized in any PRMS categories by the lender under this type of agreement.
10.5.6 Production Loans, Forward Sales, and Similar Arrangements. There are a variety of
forms of transactions that involve the advance of funds to the owner of an interest in an oil and
gas property in exchange for the right to receive the cash proceeds of production, or the
production itself, arising from the future operation of the property. In such transactions, the
owner almost invariably has a future performance obligation, the outcome of which is uncertain
to some degree. Determination of whether the transaction represents a sale or financing rests on
the particular circumstances of each case.
If the risks associated with future production, particularly those related to ultimate recovery
and price, remain primarily with the owner, the transaction should be accounted for as financing
or contingent financing. In such circumstances, the repayment obligation will normally be
defined in monetary terms and would not enable recognition of reserves and resources under
PRMS. If the risks associated with future production, particularly those related to ultimate
recovery and price, rest primarily with the purchaser, the transaction should be accounted for
either as a contingent sale or as a disposal of fixed assets. Reserves and resources would be
recognized under PRMS by the purchaser. The ability to report reserves to applicable
government agencies may be permissible; however, the specific accounting standards for the
jurisdiction should be consulted for appropriate treatment.
10.5.7 Carried Interests. A carried interest is an agreement under which one party (the carrying
party) agrees to pay for a portion or all of the preproduction costs of another party (the carried
party) on a license in which both own a portion of the working interest. This arises when the
carried party is either unwilling to bear the risk of exploration or is unable to fund the cost of
exploration or development directly. Owners may enter into carried-interest arrangements with
existing or incoming joint venture partners at the exploration stage, the development stage, or
both.
Guidelines for Application of the Petroleum Resources Management System
182
If the property becomes productive, then the carrying party will be reimbursed either (a) in
cash out of the proceeds of the share of production attributable to the carried party or (b) by
receiving a disproportionately high share of the production until the carried costs have been
recovered. The carrying party normally recognizes the additional production received in one or
more of the PRMS reserves categories. If project maturity is not sufficient to classify the
amounts as Reserves, the PRMS resources categories would be used according to the agreed
reimbursement terms.
10.5.8 Purchase Contracts. A contract to purchase oil and gas provides the right to purchase a
specified volume at an agreed price for a defined term. Under purchase contracts, exposure to
technical and market risks are borne by the seller. While a purchase or supply contract can
provide long-term access to reserves and resources through production, it does not convey the
right to extract, nor does it convey a financial interest in the reserves. Consequently, reserves and
resources would not be recognized under PRMS for this type of agreement.
10.5.9 Production Payments and Conveyances. In addition to the contracts and agreements
noted previously, there is a wide range of arrangements that have features of property trades,
loans, and production purchase contracts. These are more commonly called production payments
and conveyances and provide terms where assets are transferred between participants, assets are
pooled, or loans are provided in return for the right to purchase volumes. In certain specific
cases, as described in Sec. 10.4.3, reserves and resources may be recognized by the purchaser of
the production payment. Fig. 10.9 gives an example of a typical conveyance.
Figure 10.9—Example Conveyance—Production Payment
100 Barrels
Volume: Specified Portion of
Production and Duration
Purchaser of
Production
Payment
Seller of
Production
Payment
Value: Agreed Payment
10.6 Example Cases
10.6.1 Base-Case Example. The following example illustrates the approach used to calculate
reserves and resources under a nonconcessionary production-sharing agreement. In this example,
the contractor develops and operates the field and is entitled to a share of production that is based
on cost recovery and profit share components. The contractor takes his share of product in-kind.
The contractor does not have ownership of the underlying resources being produced but does
earn an economic interest by virtue of the exposure to technical, financial, and operational risks
and is therefore able to recognize reserves and resources for the project under PRMS. Due to the
difficulty in predicting prices, this example uses a base case oil price of USD 60 and sensitivity
cases USD 10 above and below this price. While these are unlikely to represent the actual prices
Resources Entitlement and Recognition
183
in effect, they do provide a good illustration of how entitlement and contract terms respond to
prices changes. The base case is a 500-million-bbl oil field, of which 400 million bbl, for the purposes of
this example, are reflected in the PRMS Proved Reserves category. The contract provides for an
initial exploration period, with the contract term lasting 20 years from the start of production.
The general field data are summarized in Table 10.2.
Table 1 0.2— Exam ple Fie ld
Field Info rm ation Su mm ary
Fie ld S ize
P rodu c tion Dur in g P S C
E xp lora tio n Co s t
Dr illing Cos t
De v e lo pm e n t C os t
50 0 m illi o n b b l
40 0 m illi o n b b l
$4 5 0 mi lli on
$6 0 0 mi lli on
$7 5 0 mi lli on
Fix ed O pe ra tin g Co s t
Va ria ble O pe rat ing Cos t
$1 ,8 0 0 m il lio n ($ 9 0 M M/yr )
$4 .5 5 / b b l
The production forecast is based on the Proved Reserves, while the remaining 100 million
bbl is captured as PRMS 1C and 2C resources. These resources are related to a potential contract
extension. In this simplified example, no additional drilling is required; therefore, there are no
Probable or Possible Reserves to migrate to the Proved category. However, in actual field
development, a portion of the reserves would likely be captured in the Probable (and perhaps
Possible) PRMS reserves categories, depending on supporting information and technical
certainty.
For example, some Probable (or Possible) Reserves may be captured for better-thanexpected recovery or perhaps for undrilled blocks where technical certainty was not sufficient to
classify the reserves as Proved. In this instance, modeling two cases, one for the Proved plus
Probable flow streams and a separate model for the Proved-only case, will give the Probable
Reserves entitlement by difference. Table 10.3 shows the project production forecast and fulllife cost summary.
Guidelines for Application of the Petroleum Resources Management System
184
Table 10.3—Project Production and Cost Schedule
Year
Annual Oil
Pro duction
(million bbl)
Expirat io n
Costs
($ MM)
Capital
($ MM)
Drilling
($ MM)
Op. Cost
($ MM)
Fixed
Op. Cost
($ MM)
Variable
Total
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
0.0
2.7
11.5
19.9
30.4
33.3
34.5
34.7
31.3
28.1
25.3
22.8
20.5
18.5
16.6
14.9
13.5
12.1
10.9
9.8
8.8
300
150
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
105
369
276
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
120
180
300
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
0
12
52
90
138
152
157
158
142
128
115
104
90
84
76
68
61
55
50
45
40
300
477
691
756
228
242
247
248
232
218
205
194
183
174
166
158
151
145
140
135
130
Total
400.0
450
750
600
1800
1820
5420
Production startup is midyear in the second year of the project and builds to a peak rate of
95,000 BOPD (34.7 million bbl annualized) in the eighth year. Project exploration costs are USD
450 million for exploratory drilling. The total development costs are USD 1,350 million for both
project facilities and development drilling. Operating costs comprise a fixed cost of USD 90
million per year and a variable cost of USD 4.55/bbl.
The contractor’s share of reserves and resources will be evaluated in the following with
evaluation for the effect of price and alternative tax treatment on recognizable reserves.
10.6.2 Production-Sharing Contract Terms—Normal Tax Treatment. The example contract
contains many common contractual terms affecting the industry today. These include royalty
payments, limitations on the revenue available for cost sharing, a fixed profit-share split, and
income taxes. The example case is a typical production-sharing agreement in which the
contractor is responsible for the field development and all exploration and development
expenses. In return, the contractor recovers investments and operating expenses out of the gross
production stream and is entitled to a share of the remaining profit oil. The contractor receives
payment in oil production and is exposed to both technical and market risks.
Fig. 10.10 shows the general terms of the contract. The contract is for a 20-year production
term with the possibility of an extension until project termination. The terms include a royalty
payment on gross production of 15%. Yearly cost recovery is limited to a maximum of 50% of
the annual gross revenue, with the remaining cost carried forward to be recovered in future years.
The contractor’s profit share is a based on a simple split: 20% to the contractor and 80% to the
host government.
Resources Entitlement and Recognition
185
Figure 10.10—Production-Sharing Contract (PSC) Base Case
Crude Price $60 /bbl
100 Barrels
Contractor
Government
Royalty 15%
22.6
Cost Recovery (50% Limit )
12.5
Profit Oil Split (20% / 80%)
15.0
49.9
35.1
Subtotal
64.9
6.2
Tax Paid on Behalf (50%)*
− 6.2
41.3
58.7
* Taxes may be paid by Government on behalf of Contractor in some PSCs. Depending on specific ter ms, the payments
may be treated as a tax credit or a revenue gross-up. In this example : Taxable income = (Profit Shar e)
10.6.3 Contractor Entitlement Calculation. The terms of a production-sharing contract
determine the contractor’s yearly entitlement or share of the project production based on the
yearly cost recovery and profit split. Table 10.3 shows the anticipated production, investment,
and cost profiles for the project. The calculation of the contractor’s revenue entitlement for the
peak year with 34.72 million bbl of production is shown in Table 10.4. At USD 60/bbl, the gross
revenue from 34.72 million bbl in Year 8 is USD 2,083 million. At a royalty rate of 15%, the
government would receive as royalty 5.2 million bbl valued at USD 312 million (before cost
recovery or profit split). The remaining USD 1,771 million would remain for cost recovery and
profit split according to the terms of the contract. In the production-sharing contract, revenue
available for cost recovery is limited to 50% after royalty, or USD 886 million. Costs and
expenses for the year total USD 248 million, including costs carried forward from previous
years. The yearly costs are fully recoverable. In the case of unrecovered costs, they would be
carried forward by the contractor for recovery in future years. The remaining revenue after
royalty and cost recovery is shared by the contractor and government according to the contract
profit split. In this case, the contractor’s profit share is USD 305 million, or 20% of the available
revenue after royalty and costs. The contractor’s revenue entitlement is the sum of the
contractor’s cost recovery and profit.
Guidelines for Application of the Petroleum Resources Management System
186
Table 10.4—Project Cost and Profit-Share Schedule
Year
Total
Revenue
($Million)
Net
Revenue
after
Royalty
($Million)
Recoverable
Costs
($Million)
Costs
Carried
Forward
($Million)
Contractor
Recovered
Costs
($Million)
Available
for Profit
Sharing
($Million)
Contractor
Profit
Share
($Million)
Contractor
Cost +
Profit
Share
($Million)
Contractor
Share %
Contractor
Entitlement
(Mbbls)
1
0
0
300
300
0
0
0
0
n/a
0.00
2
161
137
477
709
69
69
14
82
51
1.37
3
687
584
691
1,108
292
292
58
351
51
5.84
4
1,192
1,014
756
1,357
507
507
101
608
51
10.14
5
1,824
1,550
228
810
775
775
155
930
51
15.50
6
1,999
1,699
242
202
850
850
170
1,020
51
16.99
7
2,069
1,759
247
0
449
1,310
262
711
34
11.85
8
2,083
1,771
248
0
248
1,523
305
553
27
9.21
9
1,875
1,594
232
0
232
1,362
272
505
27
8.41
10
1,688
1,434
218
0
218
1,216
243
461
27
7.69
11
1,519
1,291
205
0
205
1,086
217
422
28
7.04
12
1,367
1,162
194
0
194
968
194
387
28
6.45
13
1,230
1,046
183
0
183
862
172
356
29
5.93
14
1,107
941
174
0
174
767
153
327
30
5.46
15
996
847
166
0
166
681
136
302
30
5.03
16
897
762
158
0
158
604
121
279
31
4.65
17
807
686
151
0
151
535
107
258
32
4.30
18
726
617
145
0
145
472
94
240
33
3.99
19
654
556
140
0
140
416
83
223
34
3.71
20
588
500
135
0
135
366
73
208
35
3.46
21
530
450
130
0
130
320
64
194
37
3.24
24,000
20,400
5,420
n/a
5,420
14,981
2,996
8416
35
140.27
Total
In the base case, the calculated average contractor cost plus profit share value in Year 8 is
USD 553 million, or about 27% of the project gross revenue. Because the cost and revenue vary
yearly, the calculated entitlement applies only to the year in question. In addition, the contractor
is obligated to pay income tax out of his share, which amounts to USD 152 million at the tax rate
of 50%.
10.6.4 Contractor Reserves Calculations. The preceding calculation represents the contractor’s
share of the yearly project revenue. In production-sharing contracts, however, the contractor
usually takes payment in kind, and the cost and profit share must be converted to an equivalent
volume of the production. The crude price may vary over the year and the method for calculating
the price for each settlement period is normally defined in the agreement. For the purposes of
this example, the crude price is assumed to be fixed at USD 60/bbl. The contractor’s crude
entitlement is equal to the profit share before tax plus cost recovery oil divided by the crude
price. For Year 8, with crude at USD 60/bbl, the contractor’s entitlement is 9.2 million bbl. In
this example, this would be reflected in the PRMS Proved Reserves category. In an actual field
development, part of these entitlement volumes may be sourced from portions of the reservoir
that are not considered Proved at the time of classification, as noted in Sec. 10.6.1. In this
situation, the non-Proved portion would be reflected in the PRMS Probable (or Possible)
categories until reclassification to Proved is justified.
This calculation provides only the contractor’s share of the annual production for the year in
question. Because reserves represent ultimate future recovery from the project, forecasts of
future production, investments, and operating expenses are required to determine future annual
entitlements. The contractor’s reserves are obtained by the summation of the estimated annual
volume entitlements over the remaining life of the project. Table 10.4 shows the forecasted
entitlements from project initiation to the end of the contract term. They were calculated with the
forecasted production schedule, exploration and drilling costs, the anticipated project investment
Resources Entitlement and Recognition
187
schedule, and the forecasted operating expense through the term of the agreement. For this case,
the contractor’s Proved PRMS Reserves are estimated at 140 million bbl, or 35% of the total
project Proved Reserves of 400 million bbl.
In the example case, prices and profit splitting were held constant over the period and the
effect of the recovery of initial capital investments can be seen on the effective net entitlement
interest. At the onset of production, entitlement (economic) interest is approximately 51% and
declines over the next several years to a low of 27% in Year 8. The entitlement interest then
increases to 37% by the end of the term. This increase is due to the natural decline in the
production rate and the need to have a greater portion of the production to reimburse fixed
operating costs. In general, production-sharing contract entitlements are highest at the point of
first production and tend to decrease as a project becomes cost current. Entitlements tend to
increase as costs increase and prices decline; however, many agreements contain “R” terms
and/or stepwise tranches that tend to reduce the profit share allocation to the contractor over
time. These take many different forms, but generally tend to be related to cumulative production
or cumulative reimbursements or to higher production rates.
10.6.5 Crude-Price Sensitivity. Contractor reserves are sensitive to the assumed production
schedule, crude-price projections, and cost forecasts. The most volatile of these factors is the
crude price. Table 10.5 demonstrates the relationship between crude price and contractor
reserves. For a USD 10/bbl increase in crude price, the contractor’s reserves decrease from 140
million to 130 million bbl. Such swings in reserves can be expected when prices are volatile. A
number of other commonly used financial metrics have also been included in Table 10.5 to
illustrate how they also change with price. Subject to specific pricing requirements in the
production-sharing-contract agreement, the ability to use average prices over a year, as provided
by PRMS, helps dampen price-related reserves changes. The contractor’s actual ultimate
recovery will, however, be determined by the weighted average crude price over the project life.
Table 10.5—Base Case, Oil Price, and Tax Sensitivity
Parameter Measured
Reserves (million bbl)
Low Case
$50 Oil Price
Base Case
$60 Oil Price
High Case
$70 Oil Price
Normal Carried
Tax
Tax
Normal Carried
Tax
Tax
Normal Carried
Tax
Tax
155
178
140
165
130
156
Cost of Finding & Dev. ($/bbl) $11.63 $10.12
$12.83 $10.89
$13.85 $11.52
Profit/bbl ($/bbl)
$14.97 $19.53
$21.36 $27.20
$28.29 $35.30
Production Costs ($/bbl)
$23.40 $20.35
$25.81 $21.91
$27.86 $23.18
Net Production Income ($/bbl) $7.48 `$13.02
$10.68 $18.13
$14.14 $23.53
NPV@10%(FASB) ($MM)
$87
$493
$260
$788
$419
$1070
SMOG/BBL ($/bbl)
$0.56
$2.78
$1.86
$4.77
$3.23
$6.85
Contractor IRR
11.9%
18.8%
15.7%
24.3%
19.5%
29.6%
Guidelines for Application of the Petroleum Resources Management System
188
10.6.6 Production-Sharing Contract—Carried Tax Treatment. In the normal case, the
contractor is obligated to pay income tax out of his share of the project profit. In such cases, the
contractor’s tax obligation impacts the project’s economics but has no impact on the reserves
calculations because reserves are calculated on a before-tax basis. In some production-sharing
agreements, however, the government or state-owned oil company agrees to pay tax on behalf of
the contractor. If the tax payment is a purely financial arrangement and the payments cannot be
attributed to a portion of the government’s production revenues, an economic interest would not
exist; therefore, no additional reserves would be recognized by the contractor. In this case, the
carried tax reserves will equal those obtained in the normal tax case, as shown in Table 10.5.
If under the terms of the contract the contractor derives a benefit from and has an economic
interest in the government’s share of hydrocarbon volumes used to fund the tax payments, those
volumes may be considered as the contractor’s reserves. Table 10.5 shows the impact on both the
project financial indicators and reserves. The contractor’s cost recovery and profit share are
computed in the standard fashion, but would now include the economic benefit related to the
taxes paid on behalf of the contractor. With a tax-paid-on-behalf arrangement, the contractor’s
base-case Proved Reserves would increase by 25 million to 165 million bbl. In an actual field
development, part of these additional entitlement volumes may be sourced from portions of the
reservoir that are not considered Proved at the time of classification. As discussed previously, the
non-Proved portion would be reflected in the PRMS Probable (or Possible) category until
reclassification to Proved is justified.
10.6.7 Reserves Sensitivity. The preceding reserves calculation illustrates the general approach
that can be used for production-sharing contracts at all levels of project maturity. It accounts for
varying yearly investment levels and the relative relationship between project costs and project
revenue. In a mature project, with relatively stable prices and the relationship between project
costs and project revenues relatively constant, some companies simplify the process by assuming
that the reserves share is equal to an average entitlement percentage. In general, this approach is
believed to be sufficiently accurate, and corrections would be applied when accounts are truedup for actual production and realizations on the regular intervals prescribed in the agreement.
10.6.8 Assessing Other Categories of Reserves and Resources. In the production-sharingcontract example case, 100 million bbl was noted to be related to the potential extension of the
original contract agreement. If significant additional new investments were required to produce
this volume and/or there was some doubt that the agreement would be extended, the related
volume would most likely be categorized as a Contingent Resource in one or more of the 1C, 2C,
or 3C scenarios, depending on the level of technical certainty. There may also be a question of
whether the same or different terms will apply to the extension. Consequently, judgment must be
used when estimating the entitlement interest that will be used to determine the net share of
PRMS resources potentially available to the contractor.
In a different scenario, if the 100 million bbl were related to potentially higher recovery
efficiency from the reservoir within the original term, and no additional debottlenecking or
development investments were required, the volume could be classified as Probable (and/or
Possible) Reserves (assuming appropriate technical certainty). To determine the effective net
interest for this Probable increment, a two-step process is commonly used. In the first step, the
Proved flowstream is evaluated using the production-sharing-contract model described in the
preceding subsections. In the second step, the forecast Proved plus Probable flowstream is then
evaluated with the production-sharing-contract model and the results from the Proved case are
subtracted. This provides the entitlement and revenues related to the discrete Probable
Resources Entitlement and Recognition
189
component. This approach can be used with multiple categories and in cases where additional
investments may also be required. It may also be used where there are multiple fields being
developed within the same production-sharing-contract ring fence.
10.7 Conclusions
Production-sharing, risked-service, and other related contracts offer the host country and the
contractor alike considerable flexibility in tailoring agreement terms to best meet sovereign and
corporate requirements.
When considering projects, each fiscal system must be reviewed on a case-by-case basis to
determine whether there is an opportunity to recognize reserves and resources for internal use,
regulatory reporting, or public disclosure. Particular care should be taken to ensure that the
contractual terms satisfy the company’s business objectives and that the impact of alternative
agreement structures is understood and considered.
The SEC Section S-X, Rule 4-10b, “Successful Efforts Method,” provides criteria and a
useful framework for determining when a mineral interest in hydrocarbon reserves and resources
exists. These criteria can be used to supplement PRMS to help determine when an economic
interest in hydrocarbons exists, allowing reserves and resources to be recognized and reported.
However, the distinction between when reserves and resources can or cannot be recognized
under many service-type contracts may not be clear and may be highly dependent on subtle
aspects of contract structure and wording.
Unlike traditional agreements, the cost-recovery terms in production-sharing, risked-service,
and other related contracts typically reduce the production entitlement (and hence reserves)
obtained by a contractor in periods of high price and increase the volumes in periods of low
price. While this ensures cost recovery, the effect on investment metrics may be counterintuitive.
The treatment of taxes and the accounting procedures used can also have a very significant
impact on the reserves and resources recognized and production reported from these contracts.
Given the complexity of these types of agreements, determination of the net company share
of hydrocarbons recognized for each PRMS classification requires economic modeling of the
flowstreams with the related costs and investments for each cumulative PRMS classification (1P,
2P, 3P and 1C, 2C, 3C). The net amount for each discrete PRMS category can then be
determined by difference from the model results (i.e., net Probable Reserves = 2P – 1P).
References
Guidelines for Application of Petroleum Reserves Definitions. 1998. Houston, Texas: Society of
Petroleum Evaluation Engineers.
Johnston, D. 1994. Petroleum Fiscal Systems and Production Sharing Contracts. Tulsa,
Oklahoma: PennWell.
Johnston, D. 1995. Different Fiscal Systems Complicate Reserves Values. Oil & Gas Journal
(29 May 1995).
SEC Guidelines, Rules, and Regulations. 1993. New York: Warren, Gorham & Lamont..
Martinez, A.R. et al. 1987. Classification and Nomenclature Systems for Petroleum and
Petroleum Reserves. Study Group Report, Houston, World Petroleum Congress.
McMichael, C.L. and Young, E.D. Effect of Production Sharing and Service Contracts on
Reserves Reporting. Paper SPE 37959 presented at the SPE Hydrocarbon Economics and
Evaluation Symposium, Dallas, 16–18 March. DOI: 10.2118/37959-MS.
Guidelines for Application of the Petroleum Resources Management System
190
Guidelines for Reporting Oil and Gas Reserves. 1995. Canberra, Australia: Australian
Petroleum Production and Exploration Association.
Reserve Recognition Under Production-Sharing and Other Nontraditional Agreements. In
Guidelines for the Evaluation of Petroleum Reserves and Resources. 2001. SPE.
http://www.spe.org/industry/reserves/docs/GuidelinesEvaluationReservesResources_2001.pdf.
Standards Pertaining to the Estimating and Auditing of Oil and Gas Reserve Information. SPE,
Richardson, Texas, USA (December 1979).
Statement of Financial Accounting Standards 19, Paragraph 11. 1977. Norwalk, Connecticut:
Financial Accounting Standards Board.
Statement of Recommended Practices—Fourth in a Series of SORPs. 1991. UK Oil Industry
Accounting Committee (January 1991).
Reference Terms
191
Reference Terms
Note: The column USED IN THESE GUIDELINES provides the chapter where the term is used (first
number) and the number of times the term appears in that chapter (number after the period). For
example, 4.12 means the term appears in Chapter 4 and is used 12 times.
TERM
REFERENCE*
1C
2007 – 2.2.2
2C
2007 – 2.2.2
3C
2007 – 2.2.2
1P
2007 – 2.2.2
2P
2007 – 2.2.2
3P
2007 – 2.2.2
Accumulation
2001 – 2.3
Aggregation
USED IN
THESE
GUIDELINES
1.1, 2.8,
4.12, 5.3,
6.1, 8.1, 10.3
1.1, 2.6,
4.12, 5.3,
6.1, 8.1, 10.3
1.1, 2.4,
4.12, 5.3,
6.1, 8.1, 10.2
1.1, 2.13,
4.18, 5.6,
6.4, 7.9, 8.8,
10.2
1.1, 2.15,
4.25, 5.6,
6.7, 7.18,
8.10, 10.2
1.1, 2.12,
4.20, 5.5,
6.2, 7.11,
8.11, 10.1
2.22, 3.6,
4.9, 5.3, 6.3,
8.37
1.1, 2.1, 4.1,
5.1, 6.26, 8.1
2007 – 3.5.1
2001 – 6
Approved for
Development
2.4
2007 – Table
I
DEFINITION
Denotes low estimate scenario of
Contingent Resources.
Denotes best estimate scenario of
Contingent Resources.
Denotes high estimate scenario of
Contingent Resources.
Taken to be equivalent to Proved Reserves;
denotes low estimate scenario of Reserves.
Taken to be equivalent to the sum of Proved
plus Probable Reserves; denotes best
estimate scenario of Reserves.
Taken to be equivalent to the sum of Proved
plus Probable plus Possible Reserves;
denotes high estimate scenario of reserves.
An individual body of naturally occurring
petroleum in a reservoir.
The process of summing reservoir (or
project) level estimates of resource
quantities to higher levels or combinations
such as field, country, or company totals.
Arithmetic summation of incremental
categories may yield different results from
probabilistic aggregation of distributions.
All necessary approvals have been
obtained; capital funds have been
committeed, and implementation of the
development project is underway.
Guidelines for Application of the Petroleum Resources Management System
TERM
REFERENCE*
Analogous
Reservoir
USED IN
THESE
GUIDELINES
2.3, 4.1
2007 – 3.4.1
Assessment
2007 – 1.2
Associated
Gas
Barrels of Oil
Equivalent
(BOE)
BasinCentered Gas
1.2, 2.11,
3.6, 4.60,
5.2, 6.3, 7.5,
8.23, 10.1
7.2, 8.2
4.12, 9.13
DEFINITION
Analogous reservoirs, as used in resources
assessments, have similar rock and fluid
properties, reservoir conditions (depth,
temperature, and pressure) and drive
mechanisms, but are typically at a more
advanced stage of development than the
reservoir of interest and thus may provide
concepts to assist in the interpretation of
more limited data and estimation of
recovery.
See Evaluation.
Associated Gas is a natural gas found in
contact with or dissolved in crude oil in the
reservoir. It can be further categorized as
Gas-Cap Gas or Solution Gas.
See Crude Oil Equivalent.
2001 – 3.7
8.2
2007 – 2.4
Behind-pipe
Reserves
none—no
occurrences
2007 – 2.1.3.1
Best Estimate
2.5, 4.36,
5.2, 6.5, 7.9,
8.1
2007 – 2.2.2
2001 – 2.5
Bitumen
192
2007 – 2.4
1.1, 8.29, 9.2
An unconventional natural gas accumulation
that is regionally pervasive and
characterized by low permeability, abnormal
pressure, gas saturated reservoirs, and lack
of a downdip water leg.
Behind-pipe reserves are expected to be
recovered from zones in existing wells,
which will require additional completion work
or future recompletion prior to the start of
production. In all cases, production can be
initiated or restored with relatively low
expenditure compared to the cost of drilling
a new well.
With respect to resource categorization, this
is considered to be the best estimate of the
quantity that will actually be recovered from
the accumulation by the project. It is the
most realistic assessment of recoverable
quantities if only a single result were
reported. If probabilistic methods are used,
there should be at least a 50% probability
(P50) that the quantities actually recovered
will equal or exceed the best estimate.
See Natural Bitumen.
Reference Terms
Buy Back
Agreement
USED IN
THESE
GUIDELINES
none—no
occurrences
Carried Interest
7.1, 10.3
TERM
REFERENCE*
2001 – 9.6.7
Chance
2007 – 1.1
Coalbed
Methane
(CBM)
2.36, 4.6,
5.1, 6.4, 8.4
8.49
2007 – 2.4
Commercial
1.1, 2.66,
3.1, 4.5, 5.2,
6.2, 7.10,
8.40
2007 – 2.1.2
and Table I
Committed
Project
none—no
occurrences
2007 – 2.1.2
and Table I
193
DEFINITION
An agreement between a host government
and a contractor under which the host pays
the contractor an agreed price for all
volumes of hydrocarbons produced by the
contractor. Pricing mechanisms typically
provide the contractor with an opportunity to
recover investment at an agreed level of
profit.
A carried interest is an agreement under
which one party (the carrying party) agrees
to pay for a portion or all of the
preproduction costs of another party (the
carried party) on a license in which both own
a portion of the working interest.
Chance is 1- Risk. (See Risk.)
Natural gas contained in coal deposits,
whether or not stored in gaseous phase.
Coalbed gas, although usually mostly
methane, may be produced with variable
amounts of inert or even non-inert gases.
(Also termed Coal Seam Gas, CSG, or
Natural Gas from Coal, NGC.)
When a project is commercial, this implies
that the essential social, environmental, and
economic conditions are met, including
political, legal, regulatory, and contractual
conditions. In addition, a project is
commercial if the degree of commitment is
such that the accumulation is expected to be
developed and placed on production within a
reasonable time frame. While 5 years is
recommended as a benchmark, a longer
time frame could be applied where, for
example, development of economic projects
are deferred at the option of the producer
for, among other things, market-related
reasons, or to meet contractual or strategic
objectives. In all cases, the justification for
classification as Reserves should be clearly
documented.
Projects status where there is a
demonstrated, firm intention to develop and
bring to production. Intention may be
demonstrated with funding/financial plans
and declaration of commerciality based on
realistic expectations of regulatory approvals
and reasonable satisfaction of other
conditions that would otherwise prevent the
project from being developed and brought to
production.
Guidelines for Application of the Petroleum Resources Management System
TERM
USED IN
THESE
GUIDELINES
4.7, 6.2, 7.2,
8.9
REFERENCE*
Completion
Completion
Interval
none—no
occurrences
Concession
7.3, 10.7
2001 – 9.6.1
Condensate
4.16, 7.1,
8.2, 9.10
2001 – 3.2
Conditions
2.1, 3.9, 4.6,
5.3, 6.4,
7.17, 8.6,
9.6, 10.1
2007 – 3.1
194
DEFINITION
Completion of a well. The process by which
a well is brought to its final classification—
basically dry hole, producer, injector, or
monitor well. A dry hole is normally plugged
and abandoned. A well deemed to be
producible of petroleum, or used as an
injector, is completed by establishing a
connection between the reservoir(s) and the
surface so that fluids can be produced from,
or injected into, the reservoir. Various
methods are utilized to establish this
connection, but they commonly involve the
installation of some combination of borehole
equipment, casing and tubing, and surface
injection or production facilities.
The specific reservoir interval(s) that is (are)
open to the borehole and connected to the
surface facilities for production or injection,
or reservoir intervals open to the wellbore
and each other for injection purposes.
A grant of access for a defined area and
time period that transfers certain
entitlements to produced hydrocarbons from
the host country to an enterprise. The
enterprise is generally responsible for
exploration, development, production, and
sale of hydrocarbons that may be
discovered. Typically granted under a
legislated fiscal system where the host
country collects taxes, fees, and sometimes
royalty on profits earned.
A mixture of hydrocarbons (mainly pentanes
and heavier) that exist in the gaseous phase
at original temperature and pressure of the
reservoir, but when produced, are in the
liquid phase at surface pressure and
temperature conditions. Condensate differs
from natural gas liquids (NGL) in two
respects: 1) NGL is extracted and recovered
in gas plants rather than lease separators or
other lease facilities; and 2) NGL includes
very light hydrocarbons (ethane, propane,
butanes) as well as the pentanes-plus that
are the main constituents of condensate.
Compare to Natural Gas Liquids (NGL)
The economic, marketing, legal,
environmental, social, and governmental
factors forecast to exist and impact the
project during the time period being
evaluated (also termed Contingencies).
Reference Terms
TERM
USED IN
THESE
GUIDELINES
7.11
REFERENCE*
Constant Case
2007 – 3.1.1
Contingency
Contingent
Project
2007 – 3.1
and Table 1
2.4, 7.1
none—no
occurrences
2007 – 2.1.2
2.27, 3.2,
4.29, 5.3,
6.2, 7.4,
8.18, 10.1
2007 – 1.1
and Table I
ContinuousType Deposit
8.1
2007 – 2.4
2001 – 2.3
none—no
occurrences
2007 – 2.4
8.3
Conventional
Gas
2007 – 2.4
Conventional
Resources
1.1, 8.3
2007 – 2.4
Conveyance
2001 – 9.6.9
DEFINITION
Modifier applied to project resources
estimates and associated cash flows when
such estimates are based on those
conditions (including costs and product
prices) that are fixed at a defined point in
time (or period average) and are applied
unchanged throughout the project life, other
than those permitted contractually. In other
words, no inflation or deflation adjustments
are made to costs, product prices, or
revenues over the evaluation period.
See Conditions.
Contingent
Resources
Conventional
Crude Oil
195
10.11
Development and production of recoverable
quantities has not been committed due to
conditions that may or may not be fulfilled.
Those quantities of petroleum estimated, as
of a given date, to be potentially recoverable
from known accumulations by application of
development projects but which are not
currently considered to be commercially
recoverable due to one or more
contingencies. Contingent Resources are a
class of discovered recoverable resources.
A petroleum accumulation that is pervasive
throughout a large area and which is not
significantly affected by hydrodynamic or
buoyancy influences. Such accumulations
are included in Unconventional Resources.
Examples of such deposits include "basincentered" gas, shale gas, gas hydrates,
natural bitumen and oil shale accumulations.
Crude Oil flowing naturally or capable of
being pumped without further processing or
dilution [see Crude Oil and compare to
Synthetic Crude Oil (SCO)].
Conventional Gas is a natural gas, trapped
by buoyancy, occurring in a normal porous
and permeable reservoir rock, either in the
gaseous phase or dissolved in crude oil, and
which technically can be produced by
normal production practices.
Conventional resources exist in discrete
petroleum accumulations related to localized
geological structural features and/or
stratigraphic conditions, typically with each
accumulation bounded by a downdip contact
with an aquifer, and which is significantly
affected by hydrodynamic influences such
as buoyancy of petroleum in water.
Certain transactions that are in substance
borrowings repayable in cash or its
Guidelines for Application of the Petroleum Resources Management System
TERM
USED IN
THESE
GUIDELINES
REFERENCE*
Cost Recovery
10.21
2001 – 9.6.2,
9.7.2
Crude Oil
4.2, 7.1, 8.3,
9.9
2001 – 3.1
Crude Oil
Equivalent
none—no
occurrences
2001 – 3.7
Cumulative
Production
Current
Economic
Conditions
2007 – 1.1
2007 – 3.1.1
4.27, 7.1,
10.2
7.3
196
DEFINITION
equivalent and shall be accounted for as
borrowings and may not qualify for the
recognition and reporting of oil and gas
reserves.
Under a typical production-sharing
agreement, the contractor is responsible for
the field development and all exploration
and development expenses. In return, the
contractor recovers costs (investments and
operating expenses) out of the gross
production stream. The contractor normally
receives payment in oil production and is
exposed to both technical and market risks.
Petroleum that exists in the liquid phase in
natural underground reservoirs and remains
liquid at atmospheric conditions of pressure
and temperature. Crude Oil may include
small amounts of nonhydrocarbons
produced with the liquids but does not
include liquids obtained from the processing
of natural gas.
Conversion of gas volumes to their oil
equivalent, customarily done on the basis of
the nominal heating content or caloric value
of the fuel. Before aggregating, the gas
volumes first must be converted to the same
temperature and pressure. Common
industry gas conversion factors usually
range between 1 barrel of oil equivalent
(BOE) = 5,600–6,000 standard cubic feet of
gas. (Also termed Barrels of Oil Equivalent.)
The sum of production of oil and gas to date
(see also Production).
Establishment of current economic
conditions should include relevant historical
petroleum prices and associated costs and
may involve a defined averaging period. The
SPE guidelines recommend that a one-year
historical average of costs and prices should
be used as the default basis of “constant
case” resources estimates and associated
project cash flows.
Reference Terms
Cushion Gas
Volume
USED IN
THESE
GUIDELINES
none—no
occurrences
Deposit
5.1, 8.14
TERM
REFERENCE*
2007 – 2.4
Deterministic
Estimate
2.2, 3.1, 6.2,
7.1
2007 – 3.5
Developed
Reserves
3.1, 6.1, 8.1
2007 – 2.1.3.2
and Table II
Developed
Producing
Reserves
2.1, 8.1
2007 – 2.1.3.2
and Table II
197
DEFINITION
With respect to underground natural gas
storage, the gas volume required in a
storage field for reservoir management
purposes and to maintain adequate
minimum storage pressure for meeting
working gas volume delivery with the
required withdrawal profile. In caverns, the
cushion gas volume is also required for
stability reasons. The cushion gas volume
may consist of recoverable and
nonrecoverable in-situ gas volumes and/or
injected gas volumes.
Material that has accumulated due to a
natural process. In resource evaluations it
identifies an accumulation of hydrocarbons
in a reservoir (see Accumulation).
The method of estimation of Reserves or
Resources is called deterministic if a
discrete estimate(s) is made based on
known geoscience, engineering, and
economic data.
Developed Reserves are expected to be
recovered from existing wells including
reserves behind pipe. Improved recovery
reserves are considered "Developed" only
after the necessary equipment has been
installed, or when the costs to do so are
relatively minor compared to the cost of a
well. Developed Reserves may be further
subclassified as Producing or NonProducing.
Developed Producing Reserves are
expected to be recovered from completion
intervals that are open and producing at the
time of the estimate. Improved recovery
reserves are considered producing only after
the improved recovery project is in
operation.
Guidelines for Application of the Petroleum Resources Management System
TERM
REFERENCE*
Developed
Non-producing
Reserves
USED IN
THESE
GUIDELINES
2.1
2007 – 2.1.3.2
and Table II
Development
not Viable
2.6, 8.3
2007 – 2.1.3.1
and Table I
Development
Pending
2.4
2007 – 2.1.3.1
and Table I
Development
Plan
2007 – 1.2
Development
Unclarified or
on Hold
1.1, 2.12,
3.2, 4.5, 5.2,
6.1, 8.4, 9.1
2.3
2007 – 2.1.3.1
and Table I
198
DEFINITION
Developed Non-Producing Reserves include
shut-in and behind-pipe Reserves. Shut-in
Reserves are expected to be recovered from
(1) completion intervals that are open at the
time of the estimate but which have not yet
started producing, (2) wells that were shut in
for market conditions or pipeline
connections, or (3) wells not capable of
production for mechanical reasons. Behindpipe Reserves are also those expected to be
recovered from zones in existing wells that
will require additional completion work or
future recompletion prior to start of
production. In all cases, production can be
initiated or restored with relatively low
expenditure compared to the cost of drilling
a new well.
A discovered accumulation for which there
are no current plans to develop or to acquire
additional data at the time due to limited
production potential. A project maturity subclass that reflects the actions required to
move a project toward commercial
production.
A discovered accumulation where project
activities are ongoing to justify commercial
development in the foreseeable future. A
project maturity subclass that reflects the
actions required to move a project toward
commercial production.
The design specifications, timing, and cost
estimates of the development project that
can include, but is not limited to, well
locations, completion techniques, drilling
methods, processing facilities, transportation
and marketing. (See also Project.)
A discovered accumulation where project
activities are on hold and/or where
justification as a commercial development
may be subject to significant delay. A
project maturity subclass that reflects the
actions required to move a project toward
commercial production.
Reference Terms
TERM
REFERENCE*
Discovered
USED IN
THESE
GUIDELINES
2.10, 4.5,
5.1, 6.1, 7.3,
8.8
2007 – 2.1.1
Discovered
Petroleum
Initially-inPlace
none—no
occurrences
2007 – 1.1
Dry Gas
8.1, 9.2
2001 – 3.2
Dry Hole
4.2, 8.1
2001 – 2.5
Economic
2007 – 3.1.2
2001 – 4.3
Economic
Interest
2.14, 4.22,
5.6, 6.2,
7.46, 8.25,
9.2, 10.8
7.2, 10.12
2001 – 9.4.1
199
DEFINITION
A discovery is one petroleum accumulation,
or several petroleum accumulations
collectively, for which one or several
exploratory wells have established through
testing, sampling, and/or logging the
existence of a significant quantity of
potentially moveable hydrocarbons. In this
context, "significant" implies that there is
evidence of a sufficient quantity of petroleum
to justify estimating the in-place volume
demonstrated by the well(s) and for
evaluating the potential for economic
recovery. (See also Known Accumulations.)
Discovered Petroleum Initially-in-Place is
that quantity of petroleum that is estimated,
as of a given date, to be contained in known
accumulations prior to production.
Discovered Petroleum Initially-In-Place may
be subdivided into Commercial, SubCommercial, and Unrecoverable, with the
estimated commercially, recoverable portion
being classified as Reserves and the
estimated subcommercial recoverable
portion being classified as Contingent
Resources.
Natural gas remaining after hydrocarbons
liquids have been removed prior to the
Reference Point (see definition). The dry
gas and removed hydrocarbon liquids are
accounted for separately in resource
assessments. It should be recognized that
this is a resource assessment definition and
not a phase behavior definition. (also called
Lean Gas)
A well found to be incapable of producing
either oil or gas in sufficient quantities to
justify completion as an oil or gas well.
In relation to petroleum Reserves and
Resources, economic refers to the situation
where the income from an operation
exceeds the expenses involved in, or
attributable to, that operation.
An Economic Interest is possessed in every
case in which an investor has acquired any
Interest in mineral in place and secures, by
any form of legal relationship, revenue
derived from the extraction of the mineral to
which he must look for a return of his capital.
Guidelines for Application of the Petroleum Resources Management System
TERM
REFERENCE*
Economic Limit
USED IN
THESE
GUIDELINES
4.27, 7.10,
8.3, 9.1
2007 – 3.1.2
2001 – 4.3
Entitlement
1.1, 7.1, 9.4,
10.30
2007 – 3.3
Entity
5.1, 7.10,
9.1, 10.1
2007 – 3.0
Estimated
Ultimate
Recovery
(EUR)
4.85, 5.1,
6.2, 7.1, 8.12
2007 – 1.1
Evaluation
1.4, 2.15,
3.2, 4.4, 5.2,
6.1, 7.42,
8.5, 10.1
2007 – 3.0
Evaluator
2.2, 4.5, 5.1,
6.1, 7.5, 8.2
2007 – 1.2,
2.1.2
Exploration
2.8, 3.4, 4.8,
5.6, 6.4, 7.3,
8.7, 10.16
200
DEFINITION
Economic limit is defined as the production
rate beyond which the net operating cash
flows (after royalties or share of production
owing to others) from a project, which may
be an individual well, lease, or entire field,
are negative.
That portion of future production (and thus
resources) legally accruing to a lessee or
contractor under the terms of the
development and production contract with a
lessor.
A legal construct capable of bearing legal
rights and obligations. In resources
evaluations this typically refers to the lessee
or contractor which is some form of legal
corporation (or consortium of corporations).
In a broader sense, an entity can be an
organization of any form and may include
governments or their agencies.
Those quantities of petroleum that are
estimated, on a given date, to be potentially
recoverable from an accumulation, plus
those quantities already produced
therefrom.
The geosciences, engineering, and
associated studies, including economic
analyses, conducted on a petroleum
exploration, development, or producing
project resulting in estimates of the
quantities that can be recovered and sold
and the associated cash flow under defined
forward conditions. Projects are classified
and estimates of derived quantities are
categorized according to applicable
guidelines. (Also termed Assessment.)
The person or group of persons responsible
for performing an evaluation of a project.
These may be employees of the entities that
have an economic interest in the project or
independent consultants contracted for
reviews and audits. In all cases, the entity
accepting the evaluation takes responsibility
for the results, including Reserves and
Resources and attributed value estimates.
Prospecting for undiscovered petroleum.
Reference Terms
TERM
REFERENCE*
Field
2001 – 2.3
Flare Gas
USED IN
THESE
GUIDELINES
1.1, 2.7,
3.18, 4.8,
5.4, 6.52,
7.6, 8.15,
9.19, 10.14
9.1
2007 – 3.2.2
Flow Test
none—no
occurrences
2007 – 2.1.1
Fluid Contacts
3.2, 4.1
2007 – 2.2.2
Forecast Case
7.15
2007 – 3.1.1
Forward Sales
10.1
2001 – 9.6.6
Fuel Gas
2007 – 3.2.2
4.1, 7.1, 9.1
201
DEFINITION
An area consisting of a single reservoir or
multiple reservoirs all grouped on, or related
to, the same individual geological structural
feature and/or stratigraphic condition. There
may be two or more reservoirs in a field that
are separated vertically by intervening
impermeable rock, laterally by local geologic
barriers, or both. The term may be defined
differently by individual regulatory
authorities.
Total volume of gas vented or burned as
part of production and processing
operations.
An operation on a well designed to
demonstrate the existence of moveable
petroleum in a reservoir by establishing flow
to the surface and/or to provide an indication
of the potential productivity of that reservoir
(such as a wireline formation test).
The surface or interface in a reservoir
separating two regions characterized by
predominant differences in fluid saturations.
Because of capillary and other phenomena,
fluid saturation change is not necessarily
abrupt or complete, nor is the surface
necessarily horizontal.
Modifier applied to project resources
estimates and associated cash flow when
such estimates are based on those
conditions (including costs and product price
schedules) forecast by the evaluator to
reasonably exist throughout the life of the
project. Inflation or deflation adjustments are
made to costs and revenues over the
evaluation period.
There are a variety of forms of transactions
that involve the advance of funds to the
owner of an interest in an oil and gas
property in exchange for the right to receive
the cash proceeds of production, or the
production itself, arising from the future
operation of the property. In such
transactions, the owner almost invariably
has a future performance obligation, the
outcome of which is uncertain to some
degree. Determination as to whether the
transaction represents a sale or financing
rests on the particular circumstances of
each case.
See Lease Fuel.
Guidelines for Application of the Petroleum Resources Management System
TERM
USED IN
THESE
GUIDELINES
none—no
occurrences
REFERENCE*
Gas Balance
2007 – 3.2.7
2001 – 3.10
Gas Cap Gas
none—no
occurrences
2001 – 6.2.2
Gas Hydrates
1.1, 8.9
2007 – 2.4
Gas Inventory
none—no
occurrences
Gas/Oil Ratio
(GOR)
4.1, 6.1, 7.1,
8.1, 9.7
2007 – 3.4.4
Gas Plant
Products
none—no
occurrences
Gas-to-Liquids
(GTL) Projects
none—no
occurrences
202
DEFINITION
In gas production operations involving
multiple working interest owners, an
imbalance in gas deliveries can occur.
These imbalances must be monitored over
time and eventually balanced in accordance
with accepted accounting procedures.
Free natural gas that overlies and is in
contact with crude oil in the reservoir. It is a
subset of Associated Gas.
Naturally occurring crystalline substances
composed of water and gas in which a solid
water lattice accommodates gas molecules
in a cagelike structure, or clathrate. At
conditions of standard temperature and
pressure (STP), one volume of saturated
methane hydrate will contain as much as
164 volumes of methane gas. Because of
this large gas-storage capacity, gas
hydrates are thought to represent an
important future source of natural gas. Gas
hydrates are included in unconventional
resources, but the technology to support
commercial production has yet to be
developed.
The sum of Working Gas Volume and
Cushion Gas Volume in underground gas
storage.
Gas to Oil Ratio (GOR) in an oil field,
calculated using measured natural gas and
crude oil volumes at stated conditions. The
gas/oil ratio may be the solution gas/oil
ration (Rs); produced gas/oil ratio (Rp); or
another suitably defined ratio of gas
production to oil production.
Gas Plant Products are natural gas liquids
(or components) recovered from natural gas
in gas processing plants and, in some
situations, from field facilities. Gas Plant
Products include ethane, propane, butanes,
butanes/propane mixtures, natural gasoline
and plant condensates, sulfur, carbon
dioxide, nitrogen, and helium.
Projects using specialized processing (e.g.,
Fischer-Tropsch synthesis) to convert
natural gas into liquid petroleum products.
Typically these projects are applied to large
gas accumulations where lack of adequate
infrastructure or local markets would make
conventional natural gas development
projects uneconomic.
Reference Terms
TERM
USED IN
THESE
GUIDELINES
none—no
occurrences
REFERENCE*
Geostatistical
Methods
2001 – 7.1
High Estimate
(Resources)
2.10, 4.27,
5.3, 7.4, 8.2
2007 – 2.2.2
2001 – 2.5
Highest Known
Hydrocarbons
4.1
2007 – 2.2.2.
Hydrocarbons
2007 – 1.1
Improved
Recovery (IR)
2.1, 3.2, 6.1,
8.7, 9.2,
10.14
2.1, 8.2, 10.1
2007 – 2.3.4
Injection
2001 – 3.5
2007 – 3.2.5
3.6, 4.36,
5.1, 7.2, 8.4,
9.4
203
DEFINITION
A variety of mathematical techniques and
processes dealing with the collection,
methods, analysis, interpretation, and
presentation of masses of geoscience and
engineering data to (mathematically)
describe the variability and uncertainties
within any reservoir unit or pool; specifically
related here to resources estimates,
including the definition of (all) well and
reservoir parameters in 1, 2, and 3
dimensions and the resultant modeling and
potential prediction of various aspects of
performance.
With respect to resource categorization, this
is considered to be an optimistic estimate of
the quantity that will actually be recovered
from an accumulation by a project. If
probabilistic methods are used, there should
be at least a 10% probability (P10) that the
quantities actually recovered will equal or
exceed the high estimate.
The shallowest occurrence of a producible
hydrocarbon accumulation as interpreted
from some combination of well log, flow test,
pressure measurement, and core data.
Hydrocarbons may or may not extend above
this depth. Modifiers are often added to
specify the type of hydrocarbons (for
instance, “highest known gas”).
Chemical compounds consisting wholly of
hydrogen and carbon.
Improved Recovery is the extraction of
additional petroleum, beyond Primary
Recovery, from naturally occurring
reservoirs by supplementing the natural
forces in the reservoir. It includes
waterflooding and gas injection for pressure
maintenance, secondary processes, tertiary
processes, and any other means of
supplementing natural reservoir recovery
processes. Improved recovery also includes
thermal and chemical processes to improve
the in-situ mobility of viscous forms of
petroleum. (Also called Enhanced
Recovery.)
The forcing, pumping, or free flow under
vacuum of substances into a porous and
permeable subsurface rock formation.
Injected substances can include either
gases or liquids.
Guidelines for Application of the Petroleum Resources Management System
TERM
REFERENCE*
Justified for
Development
USED IN
THESE
GUIDELINES
2.5, 7.1
DEFINITION
2007 – 2.1.3.1
and Table I
Kerogen
8.3
Known
Accumulation
2.1, 3.2, 8.1
2007 – 2.1.1
2001 – 2.2
Lead
2.1
2007 – 2.1.3.1
and Table I
Lease
Condensate
none—no
occurrences
Lease Fuel
9.1
204
2007 – 3.2.2
Implementation of the development project
is justified on the basis of reasonable
forecast commercial conditions at the time of
reporting and that there are reasonable
expectations that all necessary
approvals/contracts will be obtained. A
project maturity subclass that reflects the
actions required to move a project toward
commercial production.
Naturally occurring, solid, insoluble organic
material that occurs in source rocks and can
yield oil or gas upon subjection to heat and
pressure. Kerogen is also defined as the
fraction of large chemical aggregates in
sedimentary organic matter that is insoluble
in solvents (in contrast, the fraction that is
soluble in organic solvents is called natural
bitumen). (See also Oil Shales.)
An accumulation is an individual body of
petroleum-in-place. The key requirement to
consider an accumulation as “known,” and
hence containing Reserves or Contingent
Resources, is that it must have been
discovered, that is, penetrated by a well that
has established through testing, sampling,
or logging the existence of a significant
quantity of recoverable hydrocarbons.
A project associated with a potential
accumulation that is currently poorly defined
and requires more data acquisition and/or
evaluation in order to be classified as a
prospect. A project maturity subclass that
reflects the actions required to move a
project toward commercial production.
Lease Condensate is condensate recovered
from produced natural gas in
gas/liquid separators or field facilities.
Oil and/or gas used for field and processing
plant operations. For consistency quantities
consumed as lease fuel should be treated
as part of shrinkage. However, regulatory
guidelines may allow lease fuel to be
included in Reserves estimates. Where
claimed as Reserves, such fuel quantities
should be reported separately from sales
and their value must be included as an
operating expense.
Reference Terms
TERM
REFERENCE*
Lease Plant
USED IN
THESE
GUIDELINES
none—no
occurrences
Liquefied
Natural Gas
(LNG) Project
9.2
Loan
Agreement
10.5
DEFINITION
2001 – 9.6.5
Low/Best/High
Estimates
2007 – 2.2.1,
2.2.2
1.1, 2.5, 3.1,
4.9, 5.1, 7.2,
8.2
Low Estimate
2.4, 4.18,
5.2, 7.5
2007 – 2.2.2
2001 – 2.5
Lowest Known
Hydrocarbons
3.1, 5.1
2007 – 2.2.2.
Marginal
Contingent
Resources
2.1
2007 – 2.1.3.3
Measurement
205
4.4, 5.4, 6.3,
8.1, 9.14
2007 – 3.0
A general term referring to processing
facilities that are dedicated to one or more
development projects and the petroleum is
processed without prior custody transfer
from the owners of the extraction project (for
gas projects, also termed “Local Gas Plant”).
Liquefied Natural Gas projects use
specialized cryogenic processing to convert
natural gas into liquid form for tanker
transport. LNG is about 1/614 the volume of
natural gas at standard temperature and
pressure.
A loan agreement is typically used by a
bank, other investor, or partner to finance all
or part of an oil and gas project.
Compensation for funds advanced is limited
to a specified interest rate.
The range of uncertainty reflects a
reasonable range of estimated potentially
recoverable volumes at varying degrees of
uncertainty (using the cumulative scenario
approach) for an individual accumulation or
a project.
With respect to resource categorization, this
is considered to be a conservative estimate
of the quantity that will actually be recovered
from the accumulation by a project. If
probabilistic methods are used, there should
be at least a 90% probability (P90) that the
quantities actually recovered will equal or
exceed the low estimate.
The deepest occurrence of a producible
hydrocarbon accumulation as interpreted
from well log, flow test, pressure
measurement, or core data.
Known (discovered) accumulations for which
a development project(s) has been
evaluated as economic or reasonably
expected to become economic but
commitment is withheld because of one or
more contingencies (e.g., lack of market
and/or infrastructure).
The process of establishing quantity (volume
or mass) and quality of petroleum products
delivered to a reference point under
conditions defined by delivery contract or
regulatory authorities.
Guidelines for Application of the Petroleum Resources Management System
TERM
REFERENCE*
Mineral Interest
USED IN
THESE
GUIDELINES
7.4, 10.6
DEFINITION
2001 – 9.3
Monte Carlo
Simulation
2.3, 6.2, 7.1
2001 – 5
2007 – 3.5
Natural
Bitumen
2.1, 8.3
2007 – 2.4
Natural Gas
1.1, 4.3, 8.4,
9.8
2007 – 3.2.3
2001 – 6.6,
9.4.4
Natural Gas
Inventory
206
none—no
occurrences
Mineral Interests in properties including (1) a
fee ownership or lease, concession, or other
interest representing the right to extract oil
or gas subject to such terms as may be
imposed by the conveyance of that interest;
(2) royalty interests, production payments
payable in oil or gas, and other nonoperating interests in properties operated by
others; and (3) those agreements with
foreign governments or authorities under
which a reporting entity participates in the
operation of the related properties or
otherwise serves as producer of the
underlying reserves (as opposed to being an
independent purchaser, broker, dealer, or
importer).
A type of stochastic mathematical simulation
that randomly and repeatedly samples input
distributions (e.g., reservoir properties) to
generate a resulting distribution (e.g.,
recoverable petroleum volumes).
Natural Bitumen is the portion of petroleum
that exists in the semisolid or solid phase in
natural deposits. In its natural state, it
usually contains sulfur, metals, and other
nonhydrocarbons. Natural Bitumen has a
viscosity greater than 10,000 milliPascals
per second (mPa.s) (or centipoises)
measured at original temperature in the
deposit and atmospheric pressure, on a gasfree basis. In its natural viscous state, it is
not normally recoverable at commercial
rates through a well and requires the
implementation of improved recovery
methods such as steam injection. Natural
Bitumen generally requires upgrading prior
to normal refining. (Also called Crude
Bitumen.)
Natural Gas is the portion of petroleum that
exists either in the gaseous phase or is in
solution in crude oil in natural underground
reservoirs, and which is gaseous at
atmospheric conditions of pressure and
temperature. Natural Gas may include some
amount of nonhydrocarbons.
With respect to underground natural gas
storage operations “inventory” is the total of
working and cushion gas volumes.
Reference Terms
TERM
REFERENCE*
Natural Gas
Liquids (NGL)
USED IN
THESE
GUIDELINES
4.2, 6.1, 7.1,
9.3
DEFINITION
2007 – A13
2001 – 3.2,
9.4.4
Natural Gas
Liquids to Gas
Ratio
none—no
occurrences
Net-back
none—no
occurrences
none—no
occurrences
Net Profits
Interest
2007 – 3.2.1
2001 – 9.4.4
Net Working
Interest
10.2
2001 – 9.6.1
NonHydrocarbon
Gas
4.1, 9.12
2007 – 3.2.4
2001 – 3.3
NonAssociated
Gas
Normal
Production
Practices
none—no
occurrences
none—no
occurrences
207
A mixture of light hydrocarbons that exist in
the gaseous phase at reservoir conditions
but are recovered as liquids in gas
processing plants. NGL differs from
condensate in two principal respects: (1)
NGL is extracted and recovered in gas
plants rather than lease separators or other
lease facilities; and (2) NGL includes very
light hydrocarbons (ethane, propane,
butanes) as well as the pentanes-plus (the
main constituent of condensates).
Natural gas liquids to gas ratio in an oil or
gas field, calculated using measured natural
gas liquids and gas volumes at stated
conditions.
Linkage of input resource to the market price
of the refined products.
An interest that receives a portion of the net
proceeds from a well, typically after all costs
have been paid.
A company’s working interest reduced by
royalties or share of production owing to
others under applicable lease and fiscal
terms. (Also called Net Revenue Interest.)
Natural occurring associated gases such as
nitrogen, carbon dioxide, hydrogen sulfide,
and helium. If nonhydrocarbon gases are
present, the reported volumes should reflect
the condition of the gas at the point of sale.
Correspondingly, the accounts will reflect
the value of the gas product at the point of
sale.
Non-Associated Gas is a natural gas found
in a natural reservoir that does not contain
crude oil.
Production practices that involve flow of
fluids through wells to surface facilities that
involve only physical separation of fluids
and, if necessary, solids. Wells can be
stimulated, using techniques including, but
not limited to, hydraulic fracturing,
acidization, various other chemical
treatments, and thermal methods, and they
can be artificially lifted (e.g., with pumps or
gas lift). Transportation methods can include
mixing with diluents to enable flow, as well
as conventional methods of compression or
pumping. Practices that involve chemical
reforming of molecules of the produced
fluids are considered manufacturing
processes.
Guidelines for Application of the Petroleum Resources Management System
TERM
REFERENCE*
Offset Well
Location
USED IN
THESE
GUIDELINES
8.4
DEFINITION
Oil Sands
8.7
Oil Shales
8.13
2007 – 2.4
On Production
2007 – 2.1.3.1
and Table 1
2.4, 3.2, 4.2,
7.3, 8.2
Operator
2.1, 4.2, 7.1,
8.2, 10.1
Overlift /
Underlift
9.5
2007 – 3.2.7
2001 – 3.9
Penetration
2007 – 1.2
208
2.1
Potential drill location adjacent to an existing
well. The offset distance may be governed
by well spacing regulations. In the absence
of well spacing regulations, technical
analysis of drainage areas may be used to
define the spacing. For Proved volumes to
be assigned to an offset well location, there
must be conclusive, unambiguous technical
data that supports the reasonable certainty
of production of hydrocarbon volumes and
sufficient legal acreage to economically
justify the development without going below
the shallower of the fluid contact or the
lowest known hydrocarbon.
Sand deposits highly saturated with natural
bitumen. Also called “Tar Sands.” Note that
in deposits such as the western Canada “oil
sands,” significant quantities of natural
bitumen may be hosted in a range of
lithologies including siltstones and
carbonates.
Shale, siltstone, and marl deposits highly
saturated with kerogen. Whether extracted
by mining or in-situ processes, the material
must be extensively processed to yield a
marketable product (synthetic crude oil).
The development project is currently
producing and selling petroleum to market.
A project status/maturity subclass that
reflects the actions required to move a
project toward commercial production.
The company or individual responsible for
managing an exploration, development, or
production operation.
Production overlift or underlift can occur in
annual records because of the necessity for
companies to lift their entitlement in parcel
sizes to suit the available shipping
schedules as agreed among the parties. At
any given financial year-end, a company
may be in overlift or underlift. Based on the
production matching the company’s
accounts, production should be reported in
accord with and equal to the liftings actually
made by the company during the year, and
not on the production entitlement for the
year.
The intersection of a wellbore with a
reservoir.
Reference Terms
TERM
REFERENCE*
Petroleum
2007 – 1.0
USED IN
THESE
GUIDELINES
1.12, 2.11,
3.1, 4.31,
5.3, 7.28,
8.18, 9.1,
10.4
DEFINITION
Petroleum
Initially-inPlace
2.2
2007 – 1.1
Pilot Project
2007 – 2.3.4,
2.4
Play
2.5, 4.6, 8.3
2.1, 8.15
2007 – 2.1.3.1
and Table 1
Pool
3.5, 6.1
Possible
Reserves (P3)
1.1, 2.5, 4.1,
5.1, 10.4
2007 – 2.2.2
and Table 3
Primary
Recovery
2.1, 4.1
Probability
2.19, 3.1,
5.44, 6.23,
7.16, 8.1
2007 – 2.2.1
209
Petroleum is defined as a naturally occurring
mixture consisting of hydrocarbons in the
gaseous, liquid, or solid phase. Petroleum
may also contain nonhydrocarbon
compounds, common examples of which are
carbon dioxide, nitrogen, hydrogen sulfide,
and sulfur. In rare cases, nonhydrocarbon
content could be greater than 50%.
Petroleum Initially-in-Place is the total
quantity of petroleum that is estimated to
exist originally in naturally occurring
reservoirs. Crude Oil-in-place, Natural Gasin-place and Natural Bitumen-in-place are
defined in the same manner (see
Resources). (Also referred as Total
Resource Base or Hydrocarbon
Endowment.)
A small-scale test or trial operation that is
used to assess the suitability of a method for
commercial application.
A project associated with a prospective
trend of potential prospects, but which
requires more data acquisition and/or
evaluation in order to define specific leads or
prospects. A project maturity subclass that
reflects the actions required to move a
project toward commercial production.
An individual and separate accumulation of
petroleum in a reservoir.
An incremental category of estimated
recoverable volumes associated with a
defined degree of uncertainty. Possible
Reserves are those additional reserves that
analysis of geoscience and engineering data
suggest are less likely to be recoverable
than Probable Reserves. The total quantities
ultimately recovered from the project have a
low probability to exceed the sum of Proved
plus Probable plus Possible (3P), which is
equivalent to the high estimate scenario.
When probabilistic methods are used, there
should be at least a 10% probability that the
actual quantities recovered will equal or
exceed the 3P estimate.
Primary recovery is the extraction of
petroleum from reservoirs utilizing only the
natural energy available in the reservoirs to
move fluids through the reservoir rock to
other points of recovery.
The extent to which an event is likely to
occur, measured by the ratio of the
favorable cases to the whole number of
Guidelines for Application of the Petroleum Resources Management System
TERM
REFERENCE*
Probabilistic
Estimate
USED IN
THESE
GUIDELINES
DEFINITION
5.3, 7.1
2007 – 3.5
Probable
Reserves
1.1, 2.4, 6.2,
8.3, 10.3
2007 – 2.2.2
and Table 3
Production
2007 – 1.1
1.1, 2.13,
3.12, 4.151,
5.12, 6.10,
7.44, 8.89,
9.42, 10.78
ProductionSharing
Contract
210
10.33
2007 – 3.3.2
2001 – 9.6.2
cases possible. SPE convention is to quote
cumulative probability of exceeding or
equaling a quantity where P90 is the small
estimate and P10 is the large estimate. (See
also Uncertainty.)
The method of estimation of Resources is
called probabilistic when the known
geoscience, engineering, and economic
data are used to generate a continuous
range of estimates and their associated
probabilities.
An incremental category of estimated
recoverable volumes associated with a
defined degree of uncertainty. Probable
Reserves are those additional Reserves that
are less likely to be recovered than Proved
Reserves but more certain to be recovered
than Possible Reserves. It is equally likely
that actual remaining quantities recovered
will be greater than or less than the sum of
the estimated Proved plus Probable
Reserves (2P). In this context, when
probabilistic methods are used, there should
be at least a 50% probability that the actual
quantities recovered will equal or exceed the
2P estimate.
Production is the cumulative quantity of
petroleum that has been actually recovered
over a defined time period. While all
recoverable resource estimates and
production are reported in terms of the sales
product specifications, raw production
quantities (sales and nonsales, including
nonhydrocarbons) are also measured to
support engineering analyses requiring
reservoir voidage calculations.
In a production-sharing contract between a
contractor and a host government, the
contractor typically bears all risk and costs
for exploration, development, and
production. In return, if exploration is
successful, the contractor is given the
opportunity to recover the incurred
investment from production, subject to
specific limits and terms. Ownership is
retained by the host government; however,
the contractor normally receives title to the
prescribed share of the volumes as they are
produced.
Reference Terms
TERM
REFERENCE*
Profit Split
USED IN
THESE
GUIDELINES
10.7
DEFINITION
2001 – 9.6.2
Project
1.2, 2.184,
3.2, 4.172,
5.5, 6.12,
7.158, 8.59,
9.10, 10.47
2007 – 1.2
2001 – 2.3
Property
2.1, 3.6, 6.3,
7.9, 8.3, 9.1,
10.11
2007 – 1.2
2001 – 9.4
Prorationing
none—no
occurrences
Prospect
2.4, 4.3, 5.9,
8.1, 10.1
2007 – 2.1.3.1
and Table 1
Prospective
Resources
2007 – 1.1
and Table 1
1.1, 2.16,
3.2, 4.8, 6.2,
7.1, 8.5
Proved
Economic
211
none—no
occurrences
2007 – 3.1.1
Under a typical production-sharing
agreement, the contractor is responsible for
the field development and all exploration
and development expenses. In return, the
contractor is entitled to a share of the
remaining profit oil or gas. The contractor
receives payment in oil or gas production
and is exposed to both technical and market
risks.
Represents the link between the petroleum
accumulation and the decision-making
process, including budget allocation. A
project may, for example, constitute the
development of a single reservoir or field, or
an incremental development in a producing
field, or the integrated development of a
group of several fields and associated
facilities with a common ownership. In
general, an individual project will represent a
specific maturity level at which a decision is
made on whether or not to proceed (i.e.,
spend money), and there should be an
associated range of estimated recoverable
resources for that project. (See also
Development Plan.)
A volume of the Earth’s crust wherein a
corporate entity or individual has contractual
rights to extract, process, and market a
defined portion of specified in-place minerals
(including petroleum). Defined in general as
an area but may have depth and/or
stratigraphic constraints. May also be
termed a lease, concession, or license.
The allocation of production among
reservoirs and wells or allocation of pipeline
capacity among shippers, etc.
A project associated with a potential
accumulation that is sufficiently well defined
to represent a viable drilling target. A project
maturity sub-class that reflects the actions
required to move a project toward
commercial production.
Those quantities of petroleum that are
estimated, as of a given date, to be
potentially recoverable from undiscovered
accumulations.
In many cases, external regulatory reporting
and/or financing requires that, even if only
the Proved Reserves estimate for the project
is actually recovered, the project will still
meet minimum economic criteria; the project
is then termed as “Proved Economic.”
Guidelines for Application of the Petroleum Resources Management System
TERM
REFERENCE*
Proved
Reserves
USED IN
THESE
GUIDELINES
1.1, 2.4, 4.2,
5.3, 6.24,
7.5, 8.4, 9.1,
10.7
DEFINITION
2007 – 2.2.2
and Table 3
Purchase
Contract
10.4
2001 – 9.6.8
Pure-Service
Contract
10.5
2001 – 9.7.5
Range of
Uncertainty
2007 – 2.2
2001 – 2.5
2.28, 3.1,
4.3, 5.4, 6.2,
8.2
Raw Natural
Gas
212
4.2
2007 – 3.2.1
An incremental category of estimated
recoverable volumes associated with a
defined degree of uncertainty Proved
Reserves are those quantities of petroleum
which, by analysis of geoscience and
engineering data, can be estimated with
reasonable certainty to be commercially
recoverable, from a given date forward, from
known reservoirs and underdefined
economic conditions, operating methods,
and government regulations. If deterministic
methods are used, the term reasonable
certainty is intended to express a high
degree of confidence that the quantities will
be recovered. If probabilistic methods are
used, there should be at least a 90%
probability that the quantities actually
recovered will equal or exceed the estimate.
Often referred to as 1P, also as “Proven.”
A contract to purchase oil and gas provides
the right to purchase a specified volume of
production at an agreed price for a defined
term.
A pure-service contract is an agreement
between a contractor and a host
government that typically covers a defined
technical service to be provided or
completed during a specific period of time.
The service company investment is typically
limited to the value of equipment, tools, and
expenses for personnel used to perform the
service. In most cases, the service
contractor’s reimbursement is fixed by the
terms of the contract with little exposure to
either project performance or market factors.
The range of uncertainty of the recoverable
and/or potentially recoverable volumes may
be represented by either deterministic
scenarios or by a probability distribution.
(See Resource Uncertainty Categories.)
Raw Natural Gas is natural gas as it is
produced from the reservoir. It includes
water vapor and varying amounts of the
heavier hydrocarbons that may liquefy in
lease facilities or gas plants and may also
contain sulfur compounds such as hydrogen
sulfide and other nonhydrocarbon gases
such as carbon dioxide, nitrogen, or helium,
but which, nevertheless, is exploitable for its
hydrocarbon content. Raw Natural Gas is
often not suitable for direct utilization by
most types of consumers.
Reference Terms
TERM
REFERENCE*
Reasonable
Certainty
USED IN
THESE
GUIDELINES
4.3, 6.2, 8.1
DEFINITION
2007 – 2.2.2
Reasonable
Expectation
7.3
2007 – 2.1.2
Reasonable
Forecast
7.1
2007 – 3.1.2
Recoverable
Resources
2007 – 1.2
2.1, 5.1, 6.1,
8.1
Recovery
Efficiency
2.4, 4.19,
5.1, 8.7, 10.1
2007 – 2.2
Reference
Point
7.1, 9.13
2007 – 3.2.1
Reserves
2007 – 1.1
1.15, 2.63,
3.16, 4.106,
5.22, 6.68,
7.50, 8.53,
9.37, 10.112
Reservoir
2001 – 2.3
Resources
2007 – 1.1
1.1, 2.15,
3.68, 4.208,
5.35, 6.55,
7.2, 8.143,
9.16, 10.3
1.10, 2.5,
3.4, 4.15,
5.5, 6.6, 7.7,
8.17, 9.2,
10.66
213
If deterministic methods for estimating
recoverable resource quantities are used,
then reasonable certainty is intended to
express a high degree of confidence that the
estimated quantities will be recovered.
Indicates a high degree of confidence (low
risk of failure) that the project will proceed
with commercial development or the
referenced event will occur.
Indicates a high degree of confidence in
predictions of future events and commercial
conditions. The basis of such forecasts
includes, but is not limited to, analysis of
historical records and published global
economic models.
Those quantities of hydrocarbons that are
estimated to be producible from discovered
or undiscovered accumulations.
A numeric expression of that portion of inplace quantities of petroleum estimated to
be recoverable by specific processes or
projects, most often represented as a
percentage.
A defined location within a petroleum
extraction and processing operation where
quantities of produced product are
measured under defined conditions prior to
custody transfer (or consumption). Also
called Point of Sale or Custody Transfer
Point.
Reserves are those quantities of petroleum
anticipated to be commercially recoverable
by application of development projects to
known accumulations from a given date
forward under defined conditions. Reserves
must further satisfy four criteria: They must
be discovered, recoverable, commercial,
and remaining (as of a given date) based on
the development project(s) applied.
A subsurface rock formation containing an
individual and separate natural accumulation
of moveable petroleum that is confined by
impermeable rocks/formations and is
characterized by a single-pressure system.
The term “resources” as used herein is
intended to encompass all quantities of
petroleum (recoverable and unrecoverable)
naturally occurring on or within the Earth’s
crust, discovered and undiscovered, plus
those quantities already produced. Further,
it includes all types of petroleum whether
currently considered “conventional” or
Guidelines for Application of the Petroleum Resources Management System
TERM
REFERENCE*
USED IN
THESE
GUIDELINES
214
DEFINITION
“unconventional” (see Total Petroleum
Initially-in-Place). (In basin potential studies,
it may be referred to as Total Resource
Base or Hydrocarbon Endowment.)
Resources
Categories
4.8, 5.1, 10.2
2007 – 2.2
and Table 3
Resources
Classes
6.1
2007 – 1.1,
2.1 and Table
1
RevenueSharing
Contract
10.3
2001 – 9.6.3
Reversionary
Interest
7.1
Risk
2.24, 3.3,
4.3, 5.6,
6.23, 7.1,
8.7, 10.23
2001 – 2.5
Risk and
Reward
10.2
2001 – 9.4
Subdivisions of estimates of resources to be
recovered by a project(s) to indicate the
associated degrees of uncertainty.
Categories reflect uncertainties in the total
petroleum remaining within the accumulation
(in-place resources), that portion of the inplace petroleum that can be recovered by
applying a defined development project or
projects, and variations in the conditions that
may impact commercial development (e.g.,
market availability, contractual changes)
Subdivisions of Resources that indicate the
relative maturity of the development projects
being applied to yield the recoverable
quantity estimates. Project maturity may be
indicated qualitatively by allocation to
classes and subclasses and/or quantitatively
by associating a project’s estimated chance
of reaching producing status.
Revenue-sharing contracts are very similar
to the production-sharing contracts
described earlier, with the exception of
contractor payment. With these contracts,
the contractor usually receives a defined
share of revenue rather than a share of the
production.
The right of future possession of an interest
in a property when a specified condition has
been met.
The probability of loss or failure. As “risk” is
generally associated with the negative
outcome, the term “chance” is preferred for
general usage to describe the probability of
a discrete event occurring.
Risk and reward associated with oil and gas
production activities stems primarily from the
variation in revenues due to technical and
economic risks. Technical risk affects a
company’s ability to physically extract and
recover hydrocarbons and is usually
dependent on a number of technical
parameters. Economic risk is a function of
the success of a project and is critically
dependent on cost, price, and political or
other economic factors.
Reference Terms
TERM
REFERENCE*
Risked-Service
Contract
USED IN
THESE
GUIDELINES
10.4
DEFINITION
2007 – 3.3.2
2001 – 9.7.4
Royalty
7.16, 9.1,
10.16
2007 – 3.3.1
2001 – 3.8
Sales
2.6, 4.3, 6.3,
7.9, 9.38,
10.3
2007 – 3.2
Shut-in
Reserves
none—no
occurrences
2007 – 2.1.3.2
and Table 2
Solution Gas
4.28, 6.3,
7.1, 8.2
Sour Natural
Gas
none—no
occurrences
2001 – 3.4
Stochastic
Estimate
215
2.1, 6.6
2001 – 5
These agreements are very similar to the
production-sharing agreements with the
exception of contractor payment, but risk is
borne by the contractor. With a riskedservice contract, the contractor usually
receives a defined share of revenue rather
than a share of the production.
Royalty refers to payments that are due to
the host government or mineral owner
(lessor) in return for depletion of the
reservoirs and the producer
(lessee/contractor) for having access to the
petroleum resources. Many agreements
allow for the producer to lift the royalty
volumes, sell them on behalf of the royalty
owner, and pay the proceeds to the owner.
Some agreements provide for the royalty to
be taken only in kind by the royalty owner.
The quantity of petroleum product delivered
at the custody transfer (reference point) with
specifications and measurement conditions
as defined in the sales contract and/or by
regulatory authorities. All recoverable
resources are estimated in terms of the
product sales quantity measurements.
Shut-in Reserves are expected to be
recovered from (1) completion intervals
which are open at the time of the estimate,
but which have not started producing; (2)
wells which were shut-in for market
conditions or pipeline connections; or (3)
wells not capable of production for
mechanical reasons.
Solution Gas is a natural gas that is
dissolved in crude oil in the reservoir at the
prevailing reservoir conditions of pressure
and temperature. It is a subset of Associated
Gas.
Sour Natural Gas is a natural gas that
contains sulfur, sulfur compounds, and/or
carbon dioxide in quantities that may require
removal for sales or effective use.
Adjective defining a process involving or
containing a random variable or variables or
involving chance or probability such as a
stochastic stimulation.
Guidelines for Application of the Petroleum Resources Management System
TERM
USED IN
THESE
GUIDELINES
2.2
REFERENCE*
Subcommercial
2007 – 2.1.2
Submarginal
Contingent
Resources
2.1
2007 – 2.1.3.3
none—no
occurrences
Sweet Natural
Gas
2001 – 3.3
Synthetic
Crude Oil
(SCO)
8.2
2001 – A12,
A13
Taxes
7.15, 8.1,
10.14
2001 – 9.4.2
Technical
Uncertainty
2.1, 4.1
2007 – 2.2
Total
Petroleum
Initially-inPlace
2.2
2007 – 1.1
216
DEFINITION
A project is Subcommercial if the degree of
commitment is such that the accumulation is
not expected to be developed and placed on
production within a reasonable time frame.
While 5 years is recommended as a
benchmark, a longer time frame could be
applied where, for example, development of
economic projects are deferred at the option
of the producer for, among other things,
market-related reasons, or to meet
contractual or strategic objectives.
Discovered subcommercial projects are
classified as Contingent Resources.
Known (discovered) accumulations for which
evaluation of development project(s)
indicated they would not meet economic
criteria, even considering reasonably
expected improvements in conditions.
Sweet Natural Gas is a natural gas that
contains no sulfur or sulfur compounds at
all, or in such small quantities that no
processing is necessary for their removal in
order that the gas may be sold.
A mixture of hydrocarbons derived by
upgrading (i.e., chemically altering) natural
bitumen from oil sands, kerogen from oil
shales, or processing of other substances
such as natural gas or coal. SCO may
contain sulfur or other nonhydrocarbon
compounds and has many similarities to
crude oil.
Obligatory contributions to the public funds,
levied on persons, property, or income by
governmental authority.
Indication of the varying degrees of
uncertainty in estimates of recoverable
quantities influenced by range of potential
in-place hydrocarbon resources within the
reservoir and the range of the recovery
efficiency of the recovery project being
applied.
Total Petroleum Initially-in-Place is generally
accepted to be all those estimated quantities
of petroleum contained in the subsurface, as
well as those quantities already produced.
This was defined previously by the WPC as
“Petroleum-in-place” and has been termed
“Resource Base” by others. Also termed
“Original-in-Place” or “Hydrocarbon
Endowment.”
Reference Terms
TERM
USED IN
THESE
GUIDELINES
2.50, 3.17,
4.28, 5.30,
6.20, 7.8,
8.18, 9.1
REFERENCE*
Uncertainty
2007 – 2.2
2001 – 2.5
Unconventional
Resources
1.1, 8.6
2007 – 2.4
Undeveloped
Reserves
2.4, 6.1, 8.2
2001 – 2.1.3.1
and Table 2
Unitization
none—no
occurrences
Unproved
Reserves
none—no
occurrences
2001 – 5.1.1
217
DEFINITION
The range of possible outcomes in a series
of estimates. For recoverable resource
assessments, the range of uncertainty
reflects a reasonable range of estimated
potentially recoverable quantities for an
individual accumulation or a project. (See
also Probability.)
Petroleum accumulations that are pervasive
throughout a large area and that are not
significantly affected by hydrodynamic
influences (also referred to as “continuoustype deposits”). Examples include coalbed
methane (CBM), basin-centered gas, shale
gas, gas hydrate, natural bitumen (tar
sands), and oil shale deposits. Typically,
such accumulations require specialized
extraction technology (e.g., dewatering of
CBM, massive fracturing programs for shale
gas, steam and/or solvents to mobilize
bitumen for in-situ recovery, and in some
cases, mining activities). Moreover, the
extracted petroleum may require significant
processing prior to sale (e.g., bitumen
upgraders).
Undeveloped Reserves are quantities
expected to be recovered through future
investments: (1) from new wells on undrilled
acreage in known accumulations, (2) from
deepening existing wells to a different (but
known) reservoir, (3) from infill wells that will
increase recovery, or (4) where a relatively
large expenditure (e.g., when compared to
the cost of drilling a new well) is required to
(a) recomplete an existing well or (b) install
production or transportation facilities for
primary or improved recovery projects.
Process whereby owners group adjoining
properties and divide reserves, production,
costs, and other factors according to their
respective entitlement to petroleum
quantities to be recovered from the shared
reservoir(s).
Unproved Reserves are based on
geoscience and/or engineering data similar
to that used in estimates of Proved
Reserves, but technical or other
uncertainties preclude such reserves being
classified as Proved. Unproved Reserves
may be further categorized as Probable
Reserves and Possible Reserves.
Guidelines for Application of the Petroleum Resources Management System
TERM
USED IN
THESE
GUIDELINES
8.1
REFERENCE*
Unrecoverable
Resources
2007 – 1.1
Upgrader
9.2
2007 – 2.4
4.3, 7.3
Well
Abandonment
Wet Gas
4.2, 8.1, 9.3
2001 – 3.2
2007 – 3.2.3
Working Gas
Volume
none—no
occurrences
218
DEFINITION
That portion of Discovered or Undiscovered
Petroleum Initially-in-Place quantities that
are estimated, as of a given date, not to be
recoverable. A portion of these quantities
may become recoverable in the future as
commercial circumstances change,
technological developments occur, or
additional data are acquired.
A general term applied to processing plants
that convert extra-heavy crude oil and
natural bitumen into lighter crude and less
viscous synthetic crude oil (SCO). While the
detailed process varies, the underlying
concept is to remove carbon through coking
or to increase hydrogen by hydrogenation
processes using catalysts.
The permanent plugging of a dry hole, an
injection well, an exploration well or a well
that no longer produces petroleum or is no
longer capable of producing petroleum
profitably. Several steps are involved in the
abandonment of a well: permission for
abandonment and procedural requirements
are secured from official agencies; the
casing is removed and salvaged if possible;
and one or more cement plugs and/or mud
are placed in the borehole to prevent
migration of fluids between the different
formations penetrated by the borehole. In
some cases, wells may be temporarily
abandoned where operations are
suspended for extended periods pending
future conversions to other applications such
as reservoir monitoring, enhanced recovery,
etc.
Wet (Rich) gas is natural gas from which no
liquids have been removed prior to the
reference point. The wet gas is accounted
for in resource assessments, and there is no
separate accounting for contained liquids. It
should be recognized that this is a resource
assessment definition and not a phase
behavior definition.
With respect to underground natural gas
storage, Working Gas Volume (WGV) is the
volume of gas in storage above the
designed level of cushion gas that can be
withdrawn/injected with the installed
subsurface and surface facilities (wells, flow
lines, etc.) subject to legal and technical
limitations (pressures, velocities, etc.).
Depending on local site conditions
Reference Terms
TERM
USED IN
THESE
GUIDELINES
REFERENCE*
219
DEFINITION
(injection/withdrawal rates, utilization hours,
etc.), the working gas volume may be cycled
more than once a year.
Working
Interest
7.1, 9.3, 10.4
2001 – 9
A company’s equity interest in a project
before reduction for royalties or production
share owed to others under the applicable
fiscal terms.
*2001 Guidelines for the Evaluation of Reserves and Resources
2007 Petroleum Resources Management System
Guidelines for Application of the Petroleum Resources Management System
220
Acknowledgments
These guidelines represent the collaboration of an international group of more than 40
experienced reserves estimation professionals who were involved in the writing, editing,
review, and preparation of this document. The SPE Oil and Gas Reserves Committee
(OGRC) gratefully acknowledges the time and effort of the following:
Author(s)
Satinder Purewal and Ron Harrell
Jim Ross
Jean-Marc Rodriguez*
Yasin Senturk
Wim J.A.M. Swinkels
Wim J.A.M. Swinkels
Yasin Senturk
Phil Chan, John Etherington, Roberto Aquilera, Chris Clarkson,
Geoff Barker, Creties Jenkins
Chapter 9
Satinder Purewal
Chapter 10
Elliott Young
Reference Terms
Kerry Scott
*With key contributions from the following SEG Oil and Gas Reserves Committee members:
Patrick Connolly, Henk-Jaap Kloosterman, James Robertson, Bruce Shang, Raphic ven der
Weiden, and Robert Withers
Chapter
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
The authors of the individual chapters have relied on their expertise regarding appropriate guidance and application examples
in relation to PRMS. The views expressed in this document do not necessarily represent the views of the employers of any of
the authors, nor of any organisations with which they may be associated. All chapters have been peer reviewed and the
complete document has been endorsed by the sponsoring organizations of PRMS.
Applications Document Committees
Delores Hinkle and Jeff Tenzer, Chairpersons of the OGRC during development and review
of these guidelines, provided required support throughout the process. The following
Applications Document Subcommittee members laid the foundations during the first two
years: Satinder Purewal (Chair), Delores Hinkle, Bernard Seiller, Stuart Filler, Stefan
Choquette, Yasin Senturk, Phil Chan, James Pearson. The contributions of the following
committees are also gratefully acknowledged.
Committee Managing Steering Committee Editing Committee—Chapter 1 Editing Committee—Chapter 2 Editing Committee—Chapter 3 Editing Committee—Chapter 4 Editing Committee—Chapter 5 Members (* denotes Committee Chair) Satinder Purewal *(SPE), Stefan Choquette (SPE), David Gold (SPEE), Ken Mallon (AAPG) Jeff Tenzer* (SPE), Delores Hinkle (SPE) Ron Harrell*(SPEE), John Lee (SPE), Anibal Martinez (WPC) John Ritter*(AAPG), Robert Withers (SEG), Dominique Marion (SPE), Tom Scott (SPE), Bruce Shang (SEG) John Etherington*(AAPG), Mike Adams (SPE), David Gold (SPEE) Rawdon Seager*(SPE), Satinder Purewal (SPE), Glenn McMaster (AAPG), Jack Schuenemayer (AAPG) Acknowledgments
Editing Committee—Chapter 6 Editing Committee—Chapter 7 Editing Committee—Chapter 8 Editing Committee—Chapter 9 Editing Committee—Chapter 10 Editing Committee—Reference Terms 221
Rawdon Seager*(SPE), Satinder Purewal (SPE), Jeff Brown (AAPG), Jack Schuenemayer (AAPG) John Etherington* (AAPG), Mike Adams (SPE), Stefan Choquette (SPE), Stuart Filler (SPEE) Phil Chan*(SPE), Guy Sistrunk (SPE), Tom Scott (SPE), Creties Jenkins (AAPG), Paul LaPointe (AAPG) Wolfgang Schollnberger*(AAPG), David Gold (SPEE), Jeff Tenzer (SPE), Satinder Purewal Elliott Young*(SPE), Stuart Filler (SPEE) Kerry Scott*(SPE), Jeff Brown (AAPG) In addition, Holly Hargadine and other SPE staff contributions are acknowledge for
providing continuous support throughout the process of developing this document. Without
SPE staff support, this project would not have been possible to bring to fruition.
Applications Document Review Process Summary (2009–11)
A review process for these Guidelines was formally adopted at the October 2009 SPE Oil
and Gas Reserves Committee (OGRC) meeting. Chapter Editing Committees were formed
for each chapter, with expert members procured from all the stakeholder societies. Clear
mandates and timelines were established for finalization and review of the chapters. Each
Chapter Editing Committee worked with the chapter author(s) to incorporate comments and
endorse the revised chapter. A Steering Committee, chaired by Satinder Purewal, oversaw the
process.
When each Chapter Editing Committee completed and endorsed its chapter, the Chairman
sent the chapter to the Steering Committee Chairman, who circulated it for comment within
the Steering Committee. Any comments from the Steering Committee were incorporated
with consultation of the Chapter Editing Committee. All chapters were completed in draft
form and posted on an online site accessible to all reviewers and authors. Final edits were
made and incorporated into one document, which was edited by SPE staff for consistency,
language, and clarity. This document was posted on the SPE website from 15 December 2010
to 15 March 2011 for industry review and comment. Prominent notice of the posting
appeared on the home page at www.SPE.org.
All comments received by SPE were circulated to the Chapter Editing Committees and
the Steering Committee. The comments were discussed at the April 2011 OGRC meeting.
Each Chapter Editing Committee worked with the author(s) for inclusion of relevant
comments. Finalized chapters, endorsed by the Chapter Editing Committees and by the
OGRC were combined into a single document. The Reference Terms were updated to ensure
consistent references to the text, as several chapters (e.g., Chaps. 3 and 8) had changed since
the draft was published on the SPE website in December.
The final document was sent to the SPE Board of Directors for review and approved on
26 June 2011. Approval from the endorsing societies (AAPG, SPEE, SEG, and WPC) was
obtained 19 October 2011. The document was published on the SPE website on 1 November
2011.
Guidelines for Application of the Petroleum Resources Management System
ADDENDUM - CORRECTIONS TO THIS DOCUMENT
The following corrections have been made since the original publication in November 2011.
Page
5, Line 6
66, Line 1
84, Line 18
119, Eq. 7.3d
134, Line 15
Original Text
Chap. 7 covers commercial
evaluations, including a
discussion on public
disclosure and regulatory
reporting under existing
regulations.
…parameters Di and n.
If a reservoir is poorly
defined, material balance
calculations or analog
methods may be used to
arrive at an estimate of
the range of RFs.
Uncertainty ranges in the
RF can often be based on
a sensitivity analysis. If a
reservoir or project is
poorly defined, material
balance calculations or
analog methods may be
used to arrive at an
estimate of the range of
RFs. Uncertainty ranges
in the RF can often be
based on a sensitivity
analysis.
DFt = 1/[MARR](t-0..5)
…industry generally
divides TGFs into (1)
basic-centered gas
accumulations (BCGA)…
Corrected Text
Date
Chap. 7 covers commercial July 2012
evaluations.
…parameters Di and b.
If a reservoir is poorly
defined, material balance
calculations or analog
methods may be used to
arrive at an estimate of the
range of RFs. Uncertainty
ranges in the RF can often
be based on a sensitivity
analysis.
July 2012
July 2012
DFt = 1/[MARR](t-0.5)
…industry generally
divides TGFs into (1)
basin-centered gas
accumulations (BCGA)…
July 2012
July 2012
222